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Docket No. RM2019-6 Public Representative Comments
BEFORE THEPOSTAL REGULATORY COMMISSION
WASHINGTON, DC 20268-0001
Periodic Reporting Docket No. RM2019-6(Proposal One)
PUBLIC REPRESENTATIVE COMMENTSON PROPOSED CHANGE IN PERIODIC REPORTING
(August 21, 2019)
I. INTRODUCTION
The Public Representative hereby provides comments pursuant to Commission
Order No. 5133.1 In that order, the Commission established the above-referenced
docket to receive comments from interested persons addressing the Postal Service’s
proposed change of analytical principles related to periodic reporting. Id. at 5. The
Postal Service filed the Petition pursuant to 39 C.F.R. § 3050.11, along with a
supporting study by Professor Michael Bradley.2 The Postal Service provided additional
information in its responses to four Chairman’s Information Requests (CHIRs), in public
1 Notice of Proposed Rulemaking on Analytical Principles Used in Periodic Reporting (Proposal One), June 25, 2019 (Order No. 5133).
2 Petition of the United States Postal Service For the Initiation of a Proceeding to Consider Proposed Changes in Analytical Principles (Proposal One), June 21, 2019 (Petition); Michael D. Bradley, Department of Economics George Washington University, “A New Study of Special Purpose Route Carrier Costs” (Proposed Study).
Docket No. RM2019-6 Public Representative Comments
and non-public library references, and in an informal response to a question posed by
the Public Representative.3
The Public Representative commends the Postal Service for using operational
data to estimate all models except the Collection model. Using operational data
provides many advantages, as discussed below, which allow the development of
significantly improved SPR variability models. The Public Representative maintains that
Proposal One meets the test for providing a greatly improved model. Nevertheless, the
Public Representative strongly recommends that the Commission not accept Proposal
One until it can be shown that the parameter estimates of the variables comprising the
volume variability estimate are jointly significant. In addition, the Commission should
either ensure the bias associated with the censored Collection model is corrected or
shown to be insignificant. Furthermore, the Public Representative recommends that the
Commission reject moving Sunday Delivery hours for full-time carriers to Regular
Delivery, and reject moving relay hours in the SPR Regular Delivery model to the
regular, non-SPR, city carrier model.
II. PROCEDURAL HISTORY
On June 25, 2019, the Commission issued a Notice of Proposed Rulemaking on
Analytical Principles Used in Periodic Reporting (Proposal One), appointed a Public
Representative, and provided interested persons with an opportunity to comment on the
Postal Service’s proposed change. Order No. 5133.
3 Response of the United States Postal Service to Public Representative Motion Regarding Variable Definitions, July 12, 2019; Responses of the United States Postal Service to Questions 1-8 of Chairman’s Information Request No. 1, July 22, 2019; Responses of the United States Postal Service to Questions 1-6 of Chairman’s Information Request No.2, July 26, 2019; Supplemental Response of the United States Postal Service to Question 4 of Chairman’s Information Request No. 2, July 31, 2019; Responses of the United States Postal Service to Questions 1-12 of Chairman’s Information Request No. 3, August 12, 2019; Responses of the United States Postal Service to Questions 1-9 of Chairman’s Information Request No. 4, August 15, 2019.
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III. SUMMARY OF PROPOSAL
Proposal One seeks to update and improve the methodology for calculating
attributable Special Purpose Route (SPR) city carrier costs. Petition at 1. In general,
SPR carriers deliver packages to addresses across a designated geographic area and
collect mail from specified collection points. Id. They perform some or all of a number of
different activities, including: organizing mail in the office; loading their vehicles; driving
to the first delivery or collection spot; driving between delivery and collection spots;
delivering or collecting mail while out of the office; returning to the office after the last
delivery or collection spot; unloading their vehicles, and completing required office work
after returning to their home office.4
The current SPR cost model is based on a special study conducted in 1996,
which did not rely upon operational data.5 Rather, data were collected from
approximately 100 routes for one week; supervisors completed forms for each carrier
indicating types of activities performed and time spent performing each activity (such as
stop time, load time, support time, and sub-categories for each of these activities), and
the number of mailpieces delivered by each carrier per day, by class of mail. Proposed
Study at 1. The Postal Service states that “[i]t is not clear that this structure was
appropriate for forming SPR cost pools,” because it “appears to [have] be[en] based
upon an effort to force fit the SPR structure into what was then the . . . letter route
structure, thus splitting SPR time into access time, load time, and network travel.”
Proposed Study at 1. According to the Postal Service, this resulted in “a somewhat
convoluted model structure.” Id.
The Postal Service asserts that the accepted model no longer reflects current
operational activities of SPR carriers due to substantial changes made to their job
activities since the current model was adopted by the Commission in 1996.6 Specifically,
SPR carrier activities have shifted from primarily performing collection activities to 4 Id.; see also Proposed Study at 4.5 Petition, Proposal One at 1; Proposed Study at 1.6 Petition at 1; see also Proposed Study at 2-3.
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delivering parcels, which operationally separates into three basic types of activities, and,
for this reason, three separate cost pools and variability models.7 The three basic
activities are: (1) Regular Delivery, which involves primarily delivering parcels Monday
through Saturday on foot routes, and approximately 2 days a week on motorized routes,
and relaying mail from one post office to another on Monday through Saturday; (2)
delivering only parcels on Sundays and on holidays (Sunday Delivery); and (3)
collecting mail from various types of collection boxes (Collection).8
In this proceeding, the Postal Service proposes to change the methodology used
to calculate the attributable costs of Special Purpose Route (SPR) by developing a new
study of SPR costs that relies upon operational data which better reflects the current
nature of SPR operations and activities.9
IV. COMMENTS
When the Postal Service proposes to change an accepted analytical principle,
the relevant standard is whether the change will “improve the quality, accuracy, or
completeness of the data or analysis of data contained in the Postal Service’s annual
periodic reports . . . .” 39 CFR § 3050.11(a). Such petitions “should include the data,
analysis, and documentation on which the proposal is based, and, where feasible,
include an estimate of the impact of the proposed change on the relevant characteristics
of affected postal products . . . .” 39 CFR § 3050(b)(1).
The Postal Service developed all of its models except the Collection model using
operational data from a variety of operational databases, in particular the Time and
Attendance System (TACS) and the Parcel Tracking and Reporting System (PTR). This
operational data is nearly census-level, meaning data for every day of the year, which
has the potential to greatly improve the accuracy of estimates, account for seasonal
variations, account for hourly wage differences among carriers making SPR deliveries,
7 Petition at 1-2; see also Proposed Study at 2.8 Id.; see also Proposed Study at 7.9 Petition at 2-3.
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and reduce the negative econometric impact of multicollinearity, a pervasive problem in
the currently-accepted city carrier models. However, matching operational databases
such as TACS to PTR and other operational databases is complicated and time
consuming. The Public Representative greatly appreciates the enormous effort the
Postal Service invested in order to develop an operational database suitable for
econometric modeling.
The Postal Service used these operational databases to first construct cost pools
appropriate for three types of SPR delivery: (1) Regular Delivery, which is the delivery
that occurs Monday through Saturday; (2) Sunday Delivery, which is delivery which
occurs on Sundays and holidays, and (3) Collection, which is the activity of collecting
mail from designated collection boxes. It then constructed datasets and determined the
factors that drive costs (“cost drivers”) for each of these activities. This allowed it to
estimate the variability of the three types of SPR delivery based on their operational
characteristics.
For reasons discussed below, the Public Representative does not currently
recommend that the Commission adopt the variabilities proposed for the Regular
Delivery, Sunday Delivery, or Collection Delivery models until two econometric issues
are resolved. First, the Commission or the Postal Service should show that the
parameters of variables used to construct the variabilities are jointly significant. Second,
the problem of censored data causing biased estimates in the Collection model must be
solved. Once these issues are resolved the Commission or the Postal Service should
re-estimate all models, variabilities, cost pools, and impact analyses, and use them to
perform a corrected impact analysis.
In addition, the Commission should not approve moving relay time in the Regular
Delivery model to regular city carriers. It should also not approve moving peak season
Sunday time to Regular Delivery for full-time carriers during only the peak month. If
these issues are resolved, the Public Representative would support Proposal One,
because it is a great improvement upon the currently-accepted SPR model.
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A. Organization of Discussion
Section IV.B will discuss the rationale the Postal Service provides for separating
SPR accrued costs into Regular Delivery, Sunday Delivery, and Collection cost pools,
as well as the data used to construct these cost pools, and will provide the Public
Representative’s evaluation of this effort. Section VI.C will discuss the construction of
the datasets the Postal Service uses to create the variables used in the three variability
models.10
B. Postal Service Construction of Data Sets For Proposed SPR Cost Pools
1. Regular Delivery Cost Pool Formation
The proposed Regular Delivery cost pool consists of Monday through Saturday
delivery time associated with SPR carriers delivering parcels, delivering express and
priority mail, and performing relay operations (in which the carrier transports mail from
one post office to another). Hours and wages associated with the Regular Delivery cost
pool are recorded in the Time and Attendance System (TACS) using the Labor
Distribution Code 23 (LDC 23), as well as an appropriate Distribution Activity (DA) Code
to link the TACS hours and LDC code to the hourly wage rates of carriers performing
Regular Delivery.
Regular Delivery is primarily performed by full-time city carriers, who deliver 61.0
percent of Regular Delivery mail, while City Carrier Assistants (CCAs) deliver 38.7
percent.11 Full-time city carriers are career employees, who are assigned a fixed route
and guaranteed to work 5 8-hour days per week. Non-career employees, such as
10 Cost development then turns to the formation of keys which distribute attributable costs from each cost pool to the products observed in each cost pool. The Public Representative does not have any concerns with the Postal Service’s choice of method to correct heteroscedastic errors with regard to pooling, and agrees that the cubic and translog specifications should not be accepted.
11 Docket No. RM2019, USPS-RM2019-6/1 - Public Material Relating to Proposal One, Cost Pool Formation Programs and Results, File “Calculating Cost Pools.FY2018 Hours and Wages.xlsx,” Tab: Cost Pool Wages.
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CCAs, are not assigned a fixed route, and have lower hourly compensation rates than
career employees. CCAs do not have a fixed route or work schedule, and are only
guaranteed between two and four hours of work for any day on which they are
scheduled and report for work.12 The work requirement profile of CCAs meshes well with
the Bradley Report’s description of carriers performing Regular Delivery work in
motorized vehicles “where SPR carriers may support letter routes with particularly high
volume[,] [which] may occur only one or two days a week and could be in support of
more than one letter route.” Proposed Study at 5.
2. Sunday and Holiday Cost Pool Formation
The proposed Sunday Delivery cost pool consists of delivery time associated
with SPR carriers delivering parcels on Sundays and holidays. It excludes activities
associated with the delivery of express mail or priority mail, as well as stopping at
collection points and relay operations. Sunday Delivery is limited to the delivery of
parcels. Proposed Study at 4, n.1, 65. The hours associated with Sunday Delivery are
recorded in TACS as LDC 23 and LDC 24, and an appropriate DA Code is used to link
Sunday Delivery hours to the wage rates of carriers delivering mail.13
A little more than 28 percent of mail delivered on Sunday during non-peak
holiday periods (such as Columbus Day) is delivered by full-time city carriers, and
nearly 71 percent of non-peak Sunday mail is delivered by CCAs. In contrast, during
peak holiday season, Full-time city carriers deliver 53.4 percent and CCAs deliver 46.4
percent of SPR Sunday Delivery mail.14
12 NALC City Carrier Assistant Rights and Benefits, published March 6, 2014, at 4, available at https://www.nalc.org/member-benefits/body/cca_rights_and_benefits.pdf.
13 Although LDC 23 is associated with Regular Delivery, Regular Delivery carriers delivery SPR parcel mail on Sundays, both during peak season and non-peak season. As discussed below, the Postal Service proposes to transfer the peak LDC 23 hours provided for Sunday Delivery to the Regular Delivery cost pool. See Proposed Study at 69.
14 Library Reference USPS-RM2019-6/1 - Public Material Relating to Proposal One, Directory 5 Cost Pool Formation Programs and Results, File “Calculating Cost Pools.FY2018 Hours and Wages.xlsx,” Tab: Cost Pool Wages.
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3. Collection Cost Pool Formation
The activities that fall into the Collection cost pool consist of driving to and
stopping at collection receptacles designated for collection or relay, collecting and
loading the collected mail into the carrier’s vehicle, and driving to a post office.
Proposed Study at 5. Once this type of carrier reaches the next designated post office,
he or she will swipe their badge or execute a “clock ring” to signal the end of that
collection or relay run. He or she may then clock into other LDCs and perform non-
collection activities, or, if the post office is his or her home office, daily collection and
relay runs are finished. The hours and wages associated with Collection are recorded in
TACS under LDC 27, and an appropriate DA Code is used to link Collection hours to the
wage rates of the carriers performing these activities.
Approximately 69 percent of Collection occurs on Monday through Saturday. The
remainder occurs on Sundays and holidays.15 Even though mail collection occurs during
what might otherwise appear to be Regular Delivery and Sunday Delivery, the activity
differs from Regular Delivery or Sunday Delivery, because it involves stopping at
locations separated by significant distance, and because mail is gathered from the
origin location in bulk and delivered to the destination location in bulk. Neither Regular
Delivery nor Sunday Delivery involves bulk pickup or delivery operations.16
15 Docket No. RM2019-6, USPS-RM2019-6/1 - Public Material Relating to Proposal One, Cost Pool Formation Programs and Results, file “Calculating Cost Pools.FY2018 Hours and Wages.xlsx,” Tab: Cost Pool Hours. It should be noted that the Postal Service does not include relay hours in this value, as will be discussed later in these Comments.
16 The Public Representative does not know the percentage of this mail delivered by Full-time city carriers versus CCAs. Full-time city carriers are probably mostly responsible for collecting mail from Blue Boxes located along their fixed daily routes on Monday through Saturday; while CCAs probably perform special collection and relay runs ranging across many regular routes on Monday through Saturday.
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4. Public Representative Comments On The Proposed Cost Pool Formation
a. Sunday Delivery Cost Pool
(1) Non-Sunday Deliveries
It is interesting to note that some unknown percentage of hours categorized as
LDC 24 (Sunday Delivery) are deliveries that actually take place on holidays. Proposed
Study at 4 n.1. Approximately 4 percent of “Sunday” mail is delivered on Monday
through Saturday. Proposed Study at 77.
This raises a question whether holiday deliveries belong in the Sunday Delivery
cost pool. The Public Representative maintains that they do, because all hours
associated with deliveries placed in the Sunday Delivery cost pool originate from “‘hubs’
which are locations specifically chosen to facilitate Sunday delivery … [and are]
accomplished through dynamic routing . . . .” Proposed Study at 65. The Public
Representative agrees it is appropriate to include holiday delivery hours in the Sunday
cost pool and have them identified as LDC 24 activities.
(2) Moving Some Peak LDC 24 Hours To LDC 23
The Postal Service proposes moving peak season LDC 24 hours incurred by full-
time city carriers from the Sunday Delivery cost pool to the Regular Delivery cost pool.17
It argues that the seasonal peak on Sunday “brings a change in the nature of Sunday
delivery activities.” Proposed Study at 66. Specifically,
During non-peak periods, Sunday delivery is done by CCA carriers, clocked into LDC 24, delivering mostly the primary competitive product. During the peak, things change. The increase in overall package volume means that there is too much package volume during Monday through Saturday for it all to be delivered on those days. As a result, regular carriers work on Sundays, in their regular delivery areas, delivering the same type of packages that they deliver during the week.
17 The seasonal peak extends from the last week of November to the end of December. Proposed Study at 81, Table 36.
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Proposed Study at 66-67 (emphasis added).
The Public Representative maintains that the Postal Service has not provided
sufficient evidence that the delivery activities performed by full-time city carriers on
Sundays undergo a change in the nature of the activity during the seasonal peak
sufficient to warrant moving the associated hours from the Sunday Delivery cost pool to
the Regular Delivery cost pool.
First, it is not correct that during non-peak periods, Sunday Delivery is only done
by CCA Carriers clocked into LDC 24. The Postal Service acknowledges that regular
carriers deliver Sunday/holiday packages during non-peak periods. Proposed Study at
77. It is true that this only accounts for approximately 4 percent of Sunday Delivery
hours, but the Postal Service does not propose transferring these hours to Regular
Delivery. If it were a matter of this delivery being a substantially different activity when
carried out by regular carriers on non-peak Sundays, then the Postal Service should
have proposed moving non-peak, as well as peak, hours to Regular Delivery.
In addition, the Bradley Report states that during the seasonal peak, regular
carriers deliver mail in their regular areas. Proposed Study at 66-67. The Postal Service
does not make clear whether delivery to regular areas is the same as delivery along
fixed routes, which is the defining characteristic of Regular Delivery. The Postal
Service’s rationale also does not discuss whether regular carriers originate parcel
delivery at hubs or at their normal SPR origin locations during the seasonal peak. In
addition, the Postal Service does not deny that the delivery stops made by regular
carriers delivering mail on Sunday are determined by “dynamic routing,” which specifies
each turn on the delivery route. If delivery stops by regular carriers performing Sunday
delivery are determined by dynamic routing, then even if the carriers are delivering
parcels in their “regular areas,” the nature of the driving, whether loopfoot, curbline, or
door delivery would be more akin to Sunday Delivery than Regular Delivery.
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b. Moving SPR Relay To City Carrier Delivery
The Postal Service proposes moving SPR relay hours from any SPR cost pool to
an unidentified City Carrier cost pool. Proposed Study at 75. It identifies SPR relay
hours by referring to two MODS operations codes within TACS. Id. The Postal Service
defends this approach by arguing that even though SPR carriers perform a relay
operation, the purpose of the operation is fulfilled by regular city carriers.
Relay activities involve transporting mail to a location where a letter route carrier can obtain it and, subsequently, deliver it. Typically, the SPR carrier transports and deposits the mail in a green relay box from which the letter route carrier later extracts it for delivery. Because these hours are caused by mail delivered on letter routes and not by SPR delivered volumes, they are appropriately attributed to letter route costs.
Id.
The Commission should reject this argument, and should reject the proposal to
move SPR relay hours to Regular Delivery. The argument advanced by the Postal
Service is reductionist, maintaining that the nature of an activity is not found in the
activity itself, but can be reduced to the nature of a subsequent activity, or “final cause.”
Using this logic, every activity and function the Postal Service performs should be
moved to delivery. Likewise, using this logic, any single segment could be the “final
cause,” to which all other costs would be transferred. The Postal Service’s logic
contradicts the rationale for developing cost segments and components, which
recognize that cost causation is linked to the nature of the activity being performed, not
to the nature of a prior or subsequent activity.
C. Datasets and variability models
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1. Datasets Used For Variability Operational Models
a. Regular Delivery and Sunday Delivery Data
The Postal Service uses data obtained from the Parcel Tracking and Reporting
System (PTR) to obtain data on delivery activities such as volume of mail delivered,
type of delivery technology used to reach delivery stops, type of action at the delivery
receptacle, and the number of collection boxes encountered. Proposed Study at 15.
These data are used to develop variability models for the Regular Delivery and Sunday
Delivery Models. The Postal Service combines hourly TACS data with PTR data to
develop the dependent and explanatory variables used for these two models. Id. at 16-
26.
b. Collection Data
Because the PTR does not record the volume of collection mail SPR carriers
retrieve on their routes (only that they stopped at a collection point), the Postal Service
used the Collection Point Management System Mail (CPMS) to obtain collection
volume. Proposed Study at 15. The CPMS data used in this docket is taken from a
special study which collects collection volume once a year for two weeks (10 weekdays)
and two additional Saturdays (4 days), totaling 14 days beginning September 9, 2017
and ending September 29, 2019. Id. at 16, 26-27. The Postal Service obtained hours
matching these days from TACS. Id. at 27.
c. Small and Large Locations
The Postal Service divides its variability analyses into separate models for large
and small SPR locations, because the nature of SPR activities in these two size groups
differ. Proposed Study at 28-31. Specifically, the Postal Service asserts that:
The pattern of activities and productivities at the small offices differs significantly from regular offices, but it important to include them in the
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SPR cost analysis. Consultation with Postal Service delivery operation experts lead to a determination that to be considered a regular SPR location, a unit needs to have at least two full-time equivalents on a daily basis. In other words, to be a regular SPR office, a location would need to incur at least 96 SPR hours across all LDCs over the six days from Monday through Saturday. Locations with less than that amount of hours would be considered small locations…[T]he pattern of activities is quite different at small locations. Regular SPR units focus primarily on delivery with only about 20 percent of incurred hours dedicated to collection.
Id. at 29-30. The Public Representative concludes that this is a reasonable distinction,
although it will reduce the likelihood that parameter estimates using the Full Quadratic
Model will be insignificant.
2. Regular Delivery Models
a. Description
The Postal Service has presented, and the Commission has accepted, a number
of carrier models which use either the Full Quadratic or the Restricted Quadratic
specification, beginning in R2005-1, and continuing through PI2017-1. The Full
Quadratic functional form is considered a “flexible functional form,” which means that
the model is specified without restrictions on the first- and second-order derivatives.
“Thus it is agnostic… about the absence or presence of scale or network economies
that lead to variabilities being less that one hundred percent.”18 In addition, it is “robust
to the existence of zero values for cost drivers or characteristic variables.” Proposed
Study at 13.
The difficulty in using the Full Quadratic model is related to its flexibility, due to
the fact that it includes not only the squared term of the (volume-related) explanatory
variables, but also the single, squared, and cross terms of all variables, including all
characteristic variables, which do not vary with volume. The result is a great expansion
of the number of explanatory variables, which are often very similar to each other. This 18 Docket No. R2005-1, USPS-T-14, Testimony Of Michael D. Bradley On Behalf Of United States
Postal Service, April 8, 2005, at 28-29 (Bradley R2005-1 Testimony).13
Docket No. RM2019-6 Public Representative Comments
often results in a high degree of multicollinearity, which reduces the significance of
many of the variables—often the variables required to calculate variability.19 The Full
Quadratic model proposed in the instant case, which is estimated for large and small
sites for each of 4 seasons, contains 65 total variables (including the intercept) for each
of the 8 Regular Delivery regressions. So, while the annual dataset contains over
150,000 observations, the number of observations available for the September
regressions is approximately 34,000, the number of observations available for the large
locations is 3,220, and the number of observations available for the small locations is
8,623—a five- to ten-fold reduction.
b. Model Specification
The Postal Service runs eight Full Quadratic regressions: one for each quarter,
for both large and small locations. The dependent variable—hours—is recorded in
TACS for each SPR carrier for six consecutive days during the third week of the month.
Proposed Study at 32. Hours begin when the carrier clocks into his or her origin SPR
location, and end when he or she returns to the office, performs end-of-day office
activities (such as distributing collection mail), and then clocks out of the office. Id. at
17-26. Daily time, obtained from TACS, thus includes office time, loading vehicle at
office time, travelling from the office to the first delivery, beginning and ending delivery
activities, performing relay and collection activities, travelling back to the office, and
finishing end-of-day office duties. Id.
Independent variables include explanatory variables, which vary with volume and
control, characteristic variables, which take time, but do not vary with volume, and
cross-product terms, which are the combination of each variable with every other
variable. The control variables in the Regular Delivery model are three sets of variables
that account for differences in the characteristics of delivery. Id. at 9-10. The first set of
characteristic variables account for different attributes of the delivery points, such as
19 See, e.g., Bradley R2005-1 Testimony, in which all cross-terms had to be dropped in order to obtain significant parameters for explanatory variables that varied with volume.
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where a parcel is delivered and the nature of the delivery actions. For example, a given
package may have a different delivery time associated with it depending upon whether
the carrier delivers it to a mailbox at a curbline delivery point, a central delivery point, or
at a customer’s door. Id. at 10-11. The second set of characteristic variables account for
attributes of accessing the mail receptacle. Id. at 11. This set of variables includes the
proportions of packages that access the mail receptacle (“In/At Mailbox,” “Front
Door/Porch,” or “Front Desk/Reception”). Id. The third set of characteristic variables
account for different demographic attributes, such as the proportion of business and/or
residential addresses to which packages are delivered. Id. The data needed to identify
parcel volumes delivered and the proportions of each of the characteristic variables are
obtained from the PTR. Id. at 11 nn.2-3.
c. Model Testing
The Postal Service develops the variables needed to estimate the Full Quadratic
Delivery Model in eight versions of the file entitled: “Estimating.Full Quadratic Regular
Delivery Eq.sas.”20 The Postal Service performs various tests on the results of these
regressions, such as assessing the extent of multi-collinearity, assessing the extent of
heteroscedasticity, considering different methods of correcting for heteroscedasticity,
and determining whether the seasonal data available requires a panel data analysis,
rather than the usual pooled cross-section/time series models used in the past, as well
as conducting tests for outlier observations which inordinately influence the regression
results. Proposed Study at 28-74.
The use of seasonal data raises the possibility that there is systematic bias
across time, due to omitted site-specific effects. The Bradley Report’s tests show “little
evidence of systematic bias due to omitted site-specific effects…recommending use of
the pooled estimator.” Id. at 57. The Public Representative concurs with this conclusion.
20 Docket No. RM2019-6, USPS-RM2019-6/1 - Public Material Relating to Proposal One, Directory 2 Regular Delivery Programs and Results, Full Quadratic Programs, “Estimating.Full Quadratic Regular Delivery Eq.sas.”
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The Postal Service also performs various tests to determine the extent of observations
that are outliers and may be recorded in error, and may exert undue influence on the
regression results. Id. at 57-65 The Postal Service’s investigations found that most of
the outliers occurred at locations with very high volumes. Id. at 63. The Postal Service
does not conclude that the observations are erroneous in some fashion. Nevertheless,
to test whether they might be erroneous, it removed these outliers, re-estimated the
regression and observed that variability estimates were not significantly changed. Id. at
64-65. It concluded “[w]ithout a clear demarcation of erroneous data, it is appropriate to
leave all the data points in the analysis data sets.” Id. at 65. The Public Representative
concurs with this conclusion.
The Postal Service also tested whether all of the cross-product terms, which
were not crossed with explanatory variables, were jointly significant. Id. at 36-37. The
test shows that they are jointly significant, which means that the combination of these
variables is significantly different from zero, improves the regression results, and should
be retained. Id. at 36.
However, the Postal Service did not run a test of joint significance on the
variables used to calculate the variability of Regular Delivery. This is an important test to
perform, especially when using a Full Quadratic Model prone to high levels of multi-
collinearity. If the parameter estimates used to calculate delivery variability are not
jointly significant, the Full Quadratic Model should be rejected.
The Public Representative ran this test for each of the eight Regular Delivery
models, using the following SAS code:
f3jtvol: test vol, vol2, volcv, volcurb, volcbu, volcent, voldoor, volbr, voliam, volfdp, volifr; run;
The SAS log shows that there is perfect collinearity between volume and at least one
other variable. The SAS logs (attached to these Comments as Appendix A), contains
this error statement:
ERROR: The TEST is not consistent or has redundant columns.
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fejtcv: test cv, cv2, volcv + cvcurb, cvcbu, 709! cvcent, cvdoor, cvbr, cviam, cvfdp, cvifr; run;
The meaning of this result is not clear, since the Public Representative performed
the same test on the Restricted Quadratic Models, with the same result. The Public
Representative investigated this issue and discovered that the “test” procedure in SAS
suffers from scaling problems at times.21 A recommendation for correcting this problem
involves standardizing the data using the code in the cite for footnote 21.
However, while standardizing data eliminates the error associated with testing for
joint significance of the parameters which are used to construct the volume variability of
the Regular Delivery and Sunday Models, the results do not yield useable results. The
Public Representative also tested the Restricted Model, and found the same problems.
In Attachment 1, the Public Representative attaches the SAS Logs which illustrate this
problem with the Joint Significance Test, as well as the problems with standardizing
data.22
Because it is necessary to ensure that the parameters which are used to
construct the various volume variabilities are jointly significant, the Public
Representative cannot recommend the Commission adopt any of the proposed Regular
or Sunday Delivery variabilities. The Public Representative recommends the Postal
Service and Commission staff further investigate and resolve this issue. The Public
Representative would like to make clear that other than the transfer of SPR Relay Costs
to regular city carrier delivery, he would have no problems with the use of the Full
Quadratic and the resulting variabilities once this problem is investigated and resolved.
3. Sunday Delivery Models
The Postal Service moves Sunday delivery hours incurred by City Carriers
working as SPR carriers during the peak season to regular city carrier delivery, but not 21 See, https://communities.sas.com/t5/Statistical-Procedures/ERROR-The-TEST-is-not-consistent-or-has-redundant-column/td-p/414798, viewed on August 20, 2019.22 Attachment 1 also contains the SAS output showing that the output using standardized data does not produce useable results.
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the delivery hours incurred during off-peak seasons. Proposed Study at 67. The Public
Representative has already criticized this procedure, and recommends that these hours
remain in whatever Sunday Delivery model is accepted. The remainder of these
comments will therefore focus on model specification, and model testing.
a. General Description
Delivering parcels on Sunday and on holidays is new to the Postal Service’s SPR
carriers. Because only parcels are delivered, and collection mail is not picked up, the
nature of the delivery activity differs from Regular Delivery, where carriers deliver
parcels along with express and priority mail over fixed routes. The Bradley Report states
that “[o]n Sundays, the Postal Service delivers packages from ‘hubs”’ which are
locations specifically chosen to facilitate Sunday delivery,” and that “Sunday delivery is
accomplished through dynamic routing, which provides a computer-generated order of
deliveries along with the associated turn-by-turn directions.” Proposed Study at 65.
b. Model Specification
The Postal Service advocates using a Full Quadratic Model for Sunday Delivery
just as for Regular Delivery, albeit one adapted to the conditions which best capture
Sunday delivery. Proposed Study at 66. The variables are very similar to those that
comprise Regular Delivery. Id. Time is measured as the sum of LDC 24 hours from
TACS for each finance number. Id. at 69. Regressions are run by season. Independent
variables are categorized by whether they are explanatory or control variables.
Explanatory variables and control variables are pulled from the PTR and are the same
as the Regular Delivery Model, except there is no placeholder variable for stopping at
collection boxes. Id. at 66. Dummy variables are constructed as they were for Regular
Delivery. Id.
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Docket No. RM2019-6 Public Representative Comments
c. Testing
Most of the analytic effort for the Sunday Delivery model is taken up by making
the case for moving peak season hours incurred by full-time city carriers to regular city
carrier delivery. The Public Representative has already criticized that effort and will not
repeat it here. The Postal Service repeats its test of joint significance for all cross
product terms not crossed with volume. Proposed Study at 34 Table 11.
d. Comments
As with the Regular Delivery model, the Postal Service does not test whether the
parameters of the variables that make up the variability calculation are jointly significant.
Once again, SAS cannot perform this joint significant test without rescaling the data.
Consequently, the Public Representative is again unable to endorse any of the Sunday
Delivery Models. He attaches the SAS logs which show that there is perfect collinearity
between volume and at least one other variable used to calculate Sunday Delivery
volume variability, in Attachment 1. Once again, the Public Representative recommends
that the Commission request the Postal Service to use this specification to determine
Sunday Delivery variabilities, use those variabilities to recalculate its “Impact Analysis,”
and resubmit this information to the Commission. Similarly, except for his opposition to
moving peak delivery hours performed by full-time carriers to Regular Delivery, the
Public Representative would have no problems with the use of the Full Quadratic and
the resulting variabilities once this problem is investigated and resolved.
4. Collection Delivery Models
a. General Description
As was the case for the Regular Delivery and Sunday Delivery models, the
Postal Service uses a Full Quadratic model to estimate the variability of Collection
Delivery. Proposed Study at 70-74. However, while time/hours are again taken from
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Docket No. RM2019-6 Public Representative Comments
TACS, the volume of collection mail and the number of collection boxes are obtained
from Collection Point Measurement System (CPMS). Proposed Study at 71.
The CPMS data used in this docket are taken from a special study which
measured the extent to which collection boxes were full. Id. at 16. It measures collection
volume once a year for two weeks (10 weekdays) and two additional Saturdays (4
days), totaling 14 days, beginning September 9, 2017 and ending September 29, 2017.
Id. at 26-27. The Postal Service obtained hours matching these days from TACS. Id. at
27. Time is measured based on LDC 27 hours from TACS .23 Regressions are run by
season. Independent variables are categorized by whether they are explanatory or
control variables. Explanatory and control variables are developed from CPMS, and
hours are matched to the dates and finance numbers present in CPMS to TACS.24
For Collection, the Full Quadratic specification is simpler than the previously-
discussed models, because there are fewer control variables. In this case, the
dependent variable remains TACS hours, but the independent variables are limited to
volume collected from designated collection boxes, the number of boxes, their squares,
and a cross-term between collection volume and number of boxes. Proposed Study at
12.
b. Testing
The Postal Service forgoes formal testing of its proposed Collection model,
perhaps because the initial Full Quadratic Specification produced a collection variability
nearly equal to zero. Proposed Study at 71. This result prompted to Postal Service to
perform various analyses of the data. Id. at 70-74. After discussing the issue with
operations experts, the Postal Service determined that the volume an average collection
box could hold was approximately 825 pieces of mail. The Postal Service found that
23 Responses Of The United States Postal Service to Questions 1-9 Of Chairman’s Information Request No. 4, question 3.b.
24 Id.20
Docket No. RM2019-6 Public Representative Comments
approximately 2.5 percent of mailboxes in the CPMS sample contained more than 825
pieces. Id. at 73.
The Report called these boxes “overstuffed.” Id. After deleting observations
where collection boxes had more than 825 pieces, the Postal Service re-ran the Full
Quadratic model, which produced a volume variability of 24.2 percent and a box
variability 27.2 percent, which the Postal Service found acceptable. Id. at 74.
c. Comments
The Public Representative has several possible concerns about the manner in
which the Postal Service concluded that it would be acceptable to delete observations
where collection boxes had more than 825 pieces on a sample day. The Report states
that:
]T]he Postal Service operations experts were asked to determine the maximum volume that could fit in a collection box. They indicated that a collection box could hold three flat tubs and that each flat tub holds 275 pieces. Thus, a theoretical maximum volume for a collection box is 825 pieces.
Id. at 73.
First, the Bradley Report does not explain why three flat tubs is a theoretical
maximum. There are many types of collection boxes identified in the CPMS dataset,
including, Jumbo Snorkel, Jumbo Standard, Priority Snorkel, Snorkel, and others.25 The
Public Representative does not know the maximum volume of each of these types of
collection boxes, but he would have been able to endorse the maximum volume of 825
if he knew which box types this estimate was based upon.26
Furthermore, the concept of a mailbox being “overstuffed,” and therefore
deserving of elimination from consideration, contains some logical flaws. First, the
25 See Docket No. ACR2014, Library Reference USPS-FY14-43, January 16, 2015).26 Anecdotal evidence suggests the Postal Service replaces overstuffed boxes in dense urban
areas with larger boxes, rather than place another box nearby to alleviate the overstuffing problem. If true, this practice suggests that there are a number of collection boxes which are easilty capable of holding more than 825 pieces of mail.
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Docket No. RM2019-6 Public Representative Comments
Bradley Report does not provide evidence that a box with more mailpieces than 825 is
overflowing and incapable of holding more mail. Nor does the report claim the data are
erroneous. Simply dropping legitimate observations results in biased estimates. The
Postal Service or Commission staff should investigate whether the use of a technique
dealing with censored data would have been feasible and capable of producing
unbiased results.
In addition, if a person cannot insert his or her mailpiece into a collection box, he
or she may find another box nearby. In this case, it would be in the same finance
number and the fact that the original box had more than 825 pieces would not affect
regression results. If enough people drops their mail in a collection box in another
finance number, the regression would produce biased parameter estimates.,
However, the biggest problem with the Postal Service’s Collection model is once
again its use of the Full Quadratic form. Once again, a joint test of the significance of
the parameters used to calculate variability shows that there is perfect collinearity
between either volume and/or box and cvbox. As before, the Public Representative
recommends the Postal Service and Commission staff further investigate and resolve
this issue, and in this case the issue of censored data. The Public Representative would
like to make clear that other than the problems associated with censored data, he would
have no problems with the use of the Full Quadratic and the resulting variabilities once
this problem is investigated and resolved.
V. RECOMMENDATIONS AND CONCLUSIONS
The Public Representative commends the Postal Service for using operational
data to estimate all models except the Collection model. Using operational data
provides many advantages, including the ability to provide annual variability updates
without performing special studies; a greater likelihood that problems associated with
multicollinearity will be minimized; greater flexibility in selecting sub-samples without
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Docket No. RM2019-6 Public Representative Comments
incurring multicollinearity problems; the ability to account for seasonal variations, and
the ability to account for hourly wage differences among carriers. The Public
Representative greatly appreciates the enormous effort the Postal Service invested in
order to develop an operational database suitable for econometric modeling.
Unfortunately, because the issue of joint significance of the parameters which are
used to construct volume variabilities appears to be unresolved, the Public
Representative does not recommend that the Commission adopt the variabilities
proposed for the Regular Delivery or Sunday Delivery models at this time. If the issue is
resolved, but results change, the Postal Service should re-estimate the Regular Delivery
and Sunday Delivery equations and submit an impact analysis based upon the new
results. Similarly, if the Commission agrees with the Public Representative that it is not
appropriate to move Regular Delivery hours performed by full-time carriers at large
locations from Sunday Delivery to Regular Delivery, or to move relay hours incurred
during Regular Delivery, the Commission should require the Postal Service to make
these corrections, re-estimate its models, and submit an impact analysis based upon
these results.
Respectfully submitted,
Lawrence FensterPublic Representative
901 New York Avenue, N.W., Suite 200
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Docket No. RM2019-6 Public Representative Comments
Washington, DC 20268-0001 Phone (202) 789-6862Email: larry.fenster@prc.gov
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Docket No. RM2019-6 Public Representative Comments
APPENDIX 1LOGS AND OUTPUT OF VARIOUS TESTS OF JOINT SIGNIFICANCE
DECEMBER LOG SHOWING DROPPING VOLUME SQUARED ALLOWS ESTIMATED VOLUME PARAMETERS ARE JOINTLY SIGNIFICANT FROM ZERO
SUNDAY LOG (FULL QUADRATIC LOG WITH JT SIGNICANCE ERROR AFTER LINE 2281
1914 Libname SUN 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.Public Files\Public1914! Folder\Directory 1 Analysis Data Sets';NOTE: Libref SUN refers to the same physical library as COL.NOTE: Libref SUN was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.Public Files\Public Folder\Directory 1 Analysis Data Sets191519161917 options nodate;1918 ods graphics off;19191920 ****************************************************;1921 **** Read in Sunday Data ************************;1922 ****************************************************;192319241925 DATA Jun_sunreg1; set sun.June_SUNANA;1926
NOTE: There were 2460 observations read from the data set SUN.JUNE_SUNANA.NOTE: The data set WORK.JUN_SUNREG1 has 2460 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.00 seconds
1927 DATA Sep_sunreg1; set SUN.sep_SUNANA;1928
NOTE: There were 2628 observations read from the data set SUN.SEP_SUNANA.NOTE: The data set WORK.SEP_SUNREG1 has 2628 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
1929 DATA Dec_sunreg1; set SUN.dec_SUNANA;
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Docket No. RM2019-6 Public Representative Comments
1930
NOTE: There were 8420 observations read from the data set SUN.DEC_SUNANA.NOTE: The data set WORK.DEC_SUNREG1 has 8420 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.32 seconds cpu time 0.01 seconds
1931 data Mar_sunreg1; set sun.mar_sunana;19321933 **** Selecting the month to be estimated ***;1934
NOTE: There were 3036 observations read from the data set SUN.MAR_SUNANA.NOTE: The data set WORK.MAR_SUNREG1 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
1935 data sunreg2; set mar_sunreg1 ;193619371938 **** Selecting the Sunday delivery observations ***;1939
NOTE: There were 3036 observations read from the data set WORK.MAR_SUNREG1.NOTE: The data set WORK.SUNREG2 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
1940 data sunreg3; set sunreg2;1941 if hours = '.' then delete;1942 if ldc = '2400';19431944 /*if ldc = '2300' or ldc = '2400';*/1945 if vol > 0;194619471948 *** Converting the characteristic variables to levels ************;1949 *** for calculating ratios for combined LDC23 and 24 observations *****;1950
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 1941:12NOTE: There were 3036 observations read from the data set WORK.SUNREG2.NOTE: The data set WORK.SUNREG3 has 2684 observations and 21 variables.NOTE: DATA statement used (Total process time):
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Docket No. RM2019-6 Public Representative Comments
real time 0.06 seconds cpu time 0.04 seconds
1951 data sunreg3; set sunreg3;1952 cbuL = cbu * DT ;1953 centL = cent * DT ;1954 curbL = curb * DT ;1955 doorL = door * DT ;1956 otherL = other * DT ;19571958 FDPl = FDP * DE ;1959 IAMl = IAM * DE ;1960 IFRl = IFR * DE ;1961 ODEl = ODE * DE ;1962 BRL = BR * DT ;196319641965 **** Cumulating data by finance number / date ****;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
1966 proc sort data=sunreg3; by fin_no date;1967
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds
1968 proc summary data=sunreg3; by fin_no date;1969 Var1970 cbuL1971 centL1972 curbL1973 doorL1974 otherL1975 FDPL1976 IAML1977 IFRL1978 ODEL1979 BRL1980 DT1981 DE
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Docket No. RM2019-6 Public Representative Comments
1982 hours1983 ndhours1984 thours1985 vol1986 ;1987 output out=sunreg3a sum=1988 cbuL1989 centL1990 curbL1991 doorL1992 otherL1993 FDPl1994 IAMl1995 IFRl1996 ODEl1997 brL1998 DT1999 DE2000 hours2001 ndhours2002 thours2003 vol20042005 mean=2006 a_cbuL2007 a_centL2008 a_curbL2009 a_doorL2010 a_otherL2011 a_FDPL2012 a_IAML2013 a_IFRL2014 a_ODEL2015 a_BRL2016 a_DT2017 a_DE2018 a_hours2019 a_ndhours2020 a_thours2021 a_vol2022 ;20232024 ;20252026 *** Calculating characteristic variables for combined LDCs ***;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3A has 2684 observations and 36 variables.NOTE: PROCEDURE SUMMARY used (Total process time): real time 0.09 seconds cpu time 0.09 seconds
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Docket No. RM2019-6 Public Representative Comments
2027 data sunreg3a; set sunreg3a;2028 cbu = cbuL / DT ;2029 cent = centL / DT ;2030 curb = curbL / DT ;2031 door = doorL / DT ;2032 other = otherL / DT ;20332034 FDP = FDPL / DE ;2035 IAM = IAML / DE ;2036 IFR = IFRL / DE ;2037 ODE = ODEL / DE ;2038 br = BRL / DT ;2039
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3A has 2684 observations and 46 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
2040 DATA sunreg3B; set sunreg3a;2041 keep fin_no date2042 cbu2043 cent2044 curb2045 door2046 other2047 FDP2048 IAM2049 IFR2050 ODE2051 br2052 DT2053 DE2054 hours2055 ndhours2056 thours2057 vol2058 ;20592060 run;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3B has 2684 observations and 18 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
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Docket No. RM2019-6 Public Representative Comments
20612062 *** Constructing variables needed for econometric equation ***;20632064206520662067 data sunreg3C; set sunreg3b;2068 vol2=vol*vol;2069 curb2 = curb * curb ;2070 cbu2 = cbu * cbu ;2071 cent2 = cent * cent ;2072 door2 = door * door ;2073 br2 = br * br ;2074 iam2 = iam * iam ;2075 fdp2 = fdp * fdp ;2076 ifr2 = ifr * ifr ;20772078 volcurb=vol*curb;2079 volcbu=vol*cbu;2080 volcent=vol*cent;2081 voldoor=vol*door;2082 volbr=vol*br;2083 voliam=vol*iam;2084 volfdp=vol*fdp;2085 volifr=vol*ifr;2086 volode=vol*ode;2087 curbcbu = curb * cbu ;2088 curbcent = curb * cent ;2089 curbdoor = curb * door ;2090 curbbr = curb * br ;2091 curbiam = curb * iam ;2092 curbfdp = curb * fdp ;2093 curbifr = curb * ifr ;20942095 cbucent = cbu * cent ;2096 cbudoor = cbu * door ;2097 cbubr = cbu * br ;2098 cbuiam = cbu * iam ;2099 cbufdp = cbu * fdp ;2100 cbuifr = cbu * ifr ;21012102 centdoor = cent * door ;2103 centbr = cent * br ;2104 centiam = cent * iam ;2105 centfdp = cent * fdp ;2106 centifr = cent * ifr ;21072108 doorbr = door * br ;2109 dooriam = door * iam ;2110 doorfdp = door * fdp ;
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Docket No. RM2019-6 Public Representative Comments
2111 doorifr = door * ifr ;21122113 briam = br * iam ;2114 brfdp = br * fdp ;2115 brifr = br * ifr ;21162117 Iamfdp = Iam * fdp ;2118 Iamifr = Iam * ifr ;21192120 fdpifr = fdp * ifr ;21212122
NOTE: There were 2684 observations read from the data set WORK.SUNREG3B.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.04 seconds
2123 proc means; var21242125 hours2126 ndhours2127 thours2128 DT2129 curb2130 cbu2131 cent2132 door2133 other2134 br2135 DE2136 IAM2137 FDP2138 IFR2139 ODE2140 vol2141 ;21422143 output out=regmean mean=2144 m_hours2145 m_ndhours2146 m_thours2147 m_DT2148 m_curb2149 m_cbu2150 m_cent2151 m_door2152 m_other2153 m_br
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Docket No. RM2019-6 Public Representative Comments
2154 m_DE2155 m_IAM2156 m_FDP2157 m_IFR2158 m_ODE2159 m_vol;216021612162
NOTE: Writing HTML Body file: sashtml10.htmNOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.REGMEAN has 1 observations and 18 variables.NOTE: PROCEDURE MEANS used (Total process time): real time 3.90 seconds cpu time 0.43 seconds
2163 proc reg data=SUNreg3C outest=quadc;2164 model thours=vol vol2 volcurb volcbu volcent voldoor volbr voliam volfdp volifr2165 curb cbu cent door br iam fdp ifr curb2 cbu2 cent2 door2 br2 iam2 fdp2 ifr22166 curbcbu2167 curbcent2168 curbdoor2169 curbbr2170 curbiam2171 curbfdp2172 curbifr2173 cbucent2174 cbudoor2175 cbubr2176 cbuiam2177 cbufdp2178 cbuifr2179 centdoor2180 centbr2181 centiam2182 centfdp2183 centifr2184 doorbr2185 dooriam2186 doorfdp2187 doorifr2188 briam2189 brfdp2190 brifr2191 Iamfdp2192 Iamifr2193 fdpifr21942195
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Docket No. RM2019-6 Public Representative Comments
21962197 /vif white ;21982199 f1: test curbcbu,2200 curbcent,2201 curbdoor,2202 curbbr,2203 curbiam,2204 curbfdp,2205 curbifr,2206 cbucent,2207 cbudoor,2208 cbubr,2209 cbuiam,2210 cbufdp,2211 cbuifr,2212 centdoor,2213 centbr,2214 centiam,2215 centfdp,2216 centifr,2217 doorbr,2218 dooriam,2219 doorfdp,2220 doorifr,2221 briam,2222 brfdp,2223 brifr,2224 Iamfdp,2225 Iamifr,2226 fdpifr;22272228 f3jtvol: test vol, vol2, volcurb, volcbu, volcent, voldoor, volbr, voliam, volfdp, volifr;2228! run;
ERROR: The TEST is not consistent or has redundant column.2229
NOTE: The data set WORK.QUADC has 1 observations and 60 variables.NOTE: PROCEDURE REG used (Total process time): real time 0.46 seconds cpu time 0.39 seconds
2230 data elasquad; merge regmean quadc(drop=_type_);22312232 phours = INTERCEPT2233 +vol*m_vol +vol2*m_vol*m_vol2234 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_vol*m_cent+voldoor*m_vol*m_door+volbr*2234! m_vol*m_br + voliam*m_vol*m_iam + volfdp*m_vol*m_fdp +volifr*m_vol*m_ifr
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Docket No. RM2019-6 Public Representative Comments
2235 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door2236 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu +cent2*m_cent*m_cent+ door2*m_door*m_door2237 + br*m_br+ br2*m_br*m_br2238 + iam*m_iam + fdp*m_fdp + ifr*m_ifr + iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp +2238! ifr2*m_ifr*m_ifr2239 + curbcbu * m_curb * m_cbu2240 + curbcent * m_curb * m_cent2241 + curbdoor * m_curb * m_door2242 + curbbr * m_curb * m_br2243 + curbiam * m_curb * m_iam2244 + curbfdp * m_curb * m_fdp2245 + curbifr * m_curb * m_ifr2246 + cbucent * m_cbu * m_cent2247 + cbudoor * m_cbu * m_door2248 + cbubr * m_cbu * m_br2249 + cbuiam * m_cbu * m_iam2250 + cbufdp * m_cbu * m_fdp2251 + cbuifr * m_cbu * m_ifr2252 + centdoor * m_cent * m_door2253 + centbr * m_cent * m_br2254 + centiam * m_cent * m_iam2255 + centfdp * m_cent * m_fdp2256 + centifr * m_cent * m_ifr2257 + doorbr * m_door * m_br2258 + dooriam * m_door * m_iam2259 + doorfdp * m_door * m_fdp2260 + doorifr * m_door * m_ifr2261 + briam * m_br * m_iam2262 + brfdp * m_br * m_fdp2263 + brifr * m_br * m_ifr2264 + Iamfdp * m_iam * m_fdp2265 + Iamifr * m_iam * m_ifr2266 + fdpifr * m_fdp * m_ifr22672268 ;2269 ;22702271 ;22722273 mtvol= vol+2*vol2*m_vol +volcurb*m_curb+volcbu*m_cbu+volcent*m_cent+voldoor*m_door++volbr*m_br +2273! voliam*m_iam + volfdp*m_fdp +volifr*m_ifr ;2274 elasvol=mtvol*m_vol/phours;2275 mtcurb=volcurb*m_vol+curb;2276 elascurb=mtcurb*m_curb/phours;2277 mcvol=60*mtvol;22782279
NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.
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Docket No. RM2019-6 Public Representative Comments
NOTE: The data set WORK.ELASQUAD has 1 observations and 83 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.06 seconds
2280 proc print data=elasquad;2281 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;2282228322842285 run;
NOTE: There were 1 observations read from the data set WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
SUNDAY LOG (FULL QUADRATIC) SCALED
NOTE: Copyright (c) 2002-2012 by SAS Institute Inc., Cary, NC, USA.NOTE: SAS (r) Proprietary Software 9.4 (TS1M0) Licensed to POSTAL REGULATORY COMMISSION, Site 70021410.NOTE: This session is executing on the X64_7PRO platform.
NOTE: Updated analytical products:
SAS/STAT 12.3 (maintenance) SAS/ETS 12.3 (maintenance)
NOTE: Additional host information:
X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation
NOTE: SAS initialization used: real time 4.04 seconds cpu time 1.39 seconds
1 Libname SUN1 ! 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.1 ! Public Files\Public Folder\Directory 1 Analysis Data Sets';NOTE: Libref SUN was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1. Public Files\Public Folder\Directory 1 Analysis Data Sets
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Docket No. RM2019-6 Public Representative Comments
234 options nodate;5 ods graphics off;67 ****************************************************;8 **** Read in Sunday Data ************************;9 ****************************************************;101112 DATA Jun_sunreg1; set sun.June_SUNANA;13
NOTE: There were 2460 observations read from the data set SUN.JUNE_SUNANA.NOTE: The data set WORK.JUN_SUNREG1 has 2460 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.04 seconds
14 DATA Sep_sunreg1; set SUN.sep_SUNANA;15
NOTE: There were 2628 observations read from the data set SUN.SEP_SUNANA.NOTE: The data set WORK.SEP_SUNREG1 has 2628 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.03 seconds
16 DATA Dec_sunreg1; set SUN.dec_SUNANA;17
NOTE: There were 8420 observations read from the data set SUN.DEC_SUNANA.NOTE: The data set WORK.DEC_SUNREG1 has 8420 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.01 seconds
18 data Mar_sunreg1; set sun.mar_sunana;1920 **** Selecting the month to be estimated ***;21
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Docket No. RM2019-6 Public Representative Comments
NOTE: There were 3036 observations read from the data set SUN.MAR_SUNANA.NOTE: The data set WORK.MAR_SUNREG1 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
22 data sunreg2; set mar_sunreg1 ;232425 **** Selecting the Sunday delivery observations ***;26
NOTE: There were 3036 observations read from the data set WORK.MAR_SUNREG1.NOTE: The data set WORK.SUNREG2 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
27 data sunreg3; set sunreg2;28 if hours = '.' then delete;29 if ldc = '2400';3031 /*if ldc = '2300' or ldc = '2400';*/32 if vol > 0;333435 *** Converting the characteristic variables to levels35 ! ************;36 *** for calculating ratios for combined LDC23 and 2436 ! observations *****;37
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 28:12NOTE: There were 3036 observations read from the data set WORK.SUNREG2.NOTE: The data set WORK.SUNREG3 has 2684 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
38 data sunreg3; set sunreg3;
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Docket No. RM2019-6 Public Representative Comments
39 cbuL = cbu * DT ;40 centL = cent * DT ;41 curbL = curb * DT ;42 doorL = door * DT ;43 otherL = other * DT ;4445 FDPl = FDP * DE ;46 IAMl = IAM * DE ;47 IFRl = IFR * DE ;48 ODEl = ODE * DE ;49 BRL = BR * DT ;505152 **** Cumulating data by finance number / date ****;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.03 seconds
53 proc sort data=sunreg3; by fin_no date;54
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
55 proc summary data=sunreg3; by fin_no date;56 Var57 cbuL58 centL59 curbL60 doorL61 otherL62 FDPL63 IAML64 IFRL65 ODEL66 BRL67 DT68 DE69 hours
38
Docket No. RM2019-6 Public Representative Comments
70 ndhours71 thours72 vol73 ;74 output out=sunreg3a sum=75 cbuL76 centL77 curbL78 doorL79 otherL80 FDPl81 IAMl82 IFRl83 ODEl84 brL85 DT86 DE87 hours88 ndhours89 thours90 vol9192 mean=93 a_cbuL94 a_centL95 a_curbL96 a_doorL97 a_otherL98 a_FDPL99 a_IAML100 a_IFRL101 a_ODEL102 a_BRL103 a_DT104 a_DE105 a_hours106 a_ndhours107 a_thours108 a_vol109 ;110111 ;112113 *** Calculating characteristic variables for combined LDCs113! ***;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3A has 2684 observations and 36 variables.NOTE: PROCEDURE SUMMARY used (Total process time):
39
Docket No. RM2019-6 Public Representative Comments
real time 0.09 seconds cpu time 0.09 seconds
114 data sunreg3a; set sunreg3a;115 cbu = cbuL / DT ;116 cent = centL / DT ;117 curb = curbL / DT ;118 door = doorL / DT ;119 other = otherL / DT ;120121 FDP = FDPL / DE ;122 IAM = IAML / DE ;123 IFR = IFRL / DE ;124 ODE = ODEL / DE ;125 br = BRL / DT ;126
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3A has 2684 observations and 46 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.00 seconds
127 DATA sunreg3B; set sunreg3a;128 keep fin_no date129 cbu130 cent131 curb132 door133 other134 FDP135 IAM136 IFR137 ODE138 br139 DT140 DE141 hours142 ndhours143 thours144 vol145 ;146147 run;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.
40
Docket No. RM2019-6 Public Representative Comments
NOTE: The data set WORK.SUNREG3B has 2684 observations and 18 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
148149 *** Constructing variables needed for econometric equation149! ***;150151152153154 data sunreg3C; set sunreg3b;155 vol2=vol*vol;156 curb2 = curb * curb ;157 cbu2 = cbu * cbu ;158 cent2 = cent * cent ;159 door2 = door * door ;160 br2 = br * br ;161 iam2 = iam * iam ;162 fdp2 = fdp * fdp ;163 ifr2 = ifr * ifr ;164165 volcurb=vol*curb;166 volcbu=vol*cbu;167 volcent=vol*cent;168 voldoor=vol*door;169 volbr=vol*br;170 voliam=vol*iam;171 volfdp=vol*fdp;172 volifr=vol*ifr;173 volode=vol*ode;174 curbcbu = curb * cbu ;175 curbcent = curb * cent ;176 curbdoor = curb * door ;177 curbbr = curb * br ;178 curbiam = curb * iam ;179 curbfdp = curb * fdp ;180 curbifr = curb * ifr ;181182 cbucent = cbu * cent ;183 cbudoor = cbu * door ;184 cbubr = cbu * br ;185 cbuiam = cbu * iam ;186 cbufdp = cbu * fdp ;187 cbuifr = cbu * ifr ;188189 centdoor = cent * door ;190 centbr = cent * br ;
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Docket No. RM2019-6 Public Representative Comments
191 centiam = cent * iam ;192 centfdp = cent * fdp ;193 centifr = cent * ifr ;194195 doorbr = door * br ;196 dooriam = door * iam ;197 doorfdp = door * fdp ;198 doorifr = door * ifr ;199200 briam = br * iam ;201 brfdp = br * fdp ;202 brifr = br * ifr ;203204 Iamfdp = Iam * fdp ;205 Iamifr = Iam * ifr ;206207 fdpifr = fdp * ifr ;208209
NOTE: There were 2684 observations read from the data set WORK.SUNREG3B.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.04 seconds
210 proc means; var211212 hours213 ndhours214 thours215 DT216 curb217 cbu218 cent219 door220 other221 br222 DE223 IAM224 FDP225 IFR226 ODE227 vol228 ;229230 output out=regmean mean=231 m_hours
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Docket No. RM2019-6 Public Representative Comments
232 m_ndhours233 m_thours234 m_DT235 m_curb236 m_cbu237 m_cent238 m_door239 m_other240 m_br241 m_DE242 m_IAM243 m_FDP244 m_IFR245 m_ODE246 m_vol;247
NOTE: Writing HTML Body file: sashtml.htmNOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.REGMEAN has 1 observations and 18 variables.NOTE: PROCEDURE MEANS used (Total process time): real time 0.59 seconds cpu time 0.36 seconds
248 data sunreg3c; set sunreg3c;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
249 proc stdize data=sunreg3c out=sunreg3cnew;250251
NOTE: No VAR statement is given. All numerical variables not named elsewhere make up the first set of variables.WARNING: The scale estimator for variable date is less than or equal to 0. Variable date will not be standardized and a missing value is assigned to its scale estimator.NOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.SUNREG3CNEW has 2684 observations and 64 variables.
43
Docket No. RM2019-6 Public Representative Comments
NOTE: PROCEDURE STDIZE used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
252 proc reg data=SUNreg3Cnew outest=quadc;253 model thours=vol vol2 volcurb volcbu volcent voldoor volbr253! voliam volfdp volifr254 curb cbu cent door br iam fdp ifr curb2 cbu2254! cent2 door2 br2 iam2 fdp2 ifr2255 curbcbu256 curbcent257 curbdoor258 curbbr259 curbiam260 curbfdp261 curbifr262 cbucent263 cbudoor264 cbubr265 cbuiam266 cbufdp267 cbuifr268 centdoor269 centbr270 centiam271 centfdp272 centifr273 doorbr274 dooriam275 doorfdp276 doorifr277 briam278 brfdp279 brifr280 Iamfdp281 Iamifr282 fdpifr283284285286 /vif white ;287288 f1: test curbcbu,289 curbcent,290 curbdoor,291 curbbr,292 curbiam,293 curbfdp,294 curbifr,295 cbucent,
44
Docket No. RM2019-6 Public Representative Comments
296 cbudoor,297 cbubr,298 cbuiam,299 cbufdp,300 cbuifr,301 centdoor,302 centbr,303 centiam,304 centfdp,305 centifr,306 doorbr,307 dooriam,308 doorfdp,309 doorifr,310 briam,311 brfdp,312 brifr,313 Iamfdp,314 Iamifr,315 fdpifr;316317 f3jtvol: test vol, vol2, volcurb, volcbu, volcent,317! voldoor, volbr, voliam, volfdp, volifr; run;
318
NOTE: The data set WORK.QUADC has 1 observations and 60 variables.NOTE: PROCEDURE REG used (Total process time): real time 0.47 seconds cpu time 0.39 seconds
319 data elasquad; merge regmean quadc(drop=_type_);320321 phours = INTERCEPT322 +vol*m_vol +vol2*m_vol*m_vol323 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_v323! ol*m_cent+voldoor*m_vol*m_door+volbr*m_vol*m_br +323! voliam*m_vol*m_iam + volfdp*m_vol*m_fdp +volifr*m_vol*m_ifr324 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door325 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu325! +cent2*m_cent*m_cent+ door2*m_door*m_door326 + br*m_br+ br2*m_br*m_br327 + iam*m_iam + fdp*m_fdp + ifr*m_ifr +327! iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp + ifr2*m_ifr*m_ifr328 + curbcbu * m_curb * m_cbu329 + curbcent * m_curb * m_cent330 + curbdoor * m_curb * m_door331 + curbbr * m_curb * m_br332 + curbiam * m_curb * m_iam
45
Docket No. RM2019-6 Public Representative Comments
333 + curbfdp * m_curb * m_fdp334 + curbifr * m_curb * m_ifr335 + cbucent * m_cbu * m_cent336 + cbudoor * m_cbu * m_door337 + cbubr * m_cbu * m_br338 + cbuiam * m_cbu * m_iam339 + cbufdp * m_cbu * m_fdp340 + cbuifr * m_cbu * m_ifr341 + centdoor * m_cent * m_door342 + centbr * m_cent * m_br343 + centiam * m_cent * m_iam344 + centfdp * m_cent * m_fdp345 + centifr * m_cent * m_ifr346 + doorbr * m_door * m_br347 + dooriam * m_door * m_iam348 + doorfdp * m_door * m_fdp349 + doorifr * m_door * m_ifr350 + briam * m_br * m_iam351 + brfdp * m_br * m_fdp352 + brifr * m_br * m_ifr353 + Iamfdp * m_iam * m_fdp354 + Iamifr * m_iam * m_ifr355 + fdpifr * m_fdp * m_ifr356357 ;358 ;359360 ;361362 mtvol= vol+2*vol2*m_vol362! +volcurb*m_curb+volcbu*m_cbu+volcent*m_cent+voldoor*m_door+362! +volbr*m_br + voliam*m_iam + volfdp*m_fdp +volifr*m_ifr ;363 elasvol=mtvol*m_vol/phours;364 mtcurb=volcurb*m_vol+curb;365 elascurb=mtcurb*m_curb/phours;366 mcvol=60*mtvol;367368
NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.NOTE: The data set WORK.ELASQUAD has 1 observations and 83 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.04 seconds
369 proc print data=elasquad;
46
Docket No. RM2019-6 Public Representative Comments
370 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;371372373374 run;
NOTE: There were 1 observations read from the data set WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.04 seconds cpu time 0.01 seconds
SUNDAY OUTPUT FULL QUADRATIC
The SAS System
The MEANS ProcedureVariable N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
curb
cbu
cent
door
other
br
DE
IAM
FDP
IFR
ODE
vol
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
33.8889909
6.3568816
40.2458725
600.5559199
0.3278959
0.1262009
0.1267505
0.3549808
0.0641718
0.0563836
600.5559199
0.3105419
0.5419724
0.0690464
0.0784393
600.5526412
36.6422673
9.9703193
44.5134535
689.1818796
0.2551530
0.1425660
0.1606803
0.2475542
0.0809493
0.0649420
689.1818796
0.2081660
0.2262575
0.0978172
0.0827383
689.1775982
0.1200000
0
0.1200000
1.0000000
0
0
0
0
-1.11022E-16
0
1.0000000
0
0
0
-8.32667E-17
1.0000000
345.9200000
122.4600000
404.7800000
6533.00
1.0000000
0.9382716
1.0000000
1.0000000
0.8000000
1.0000000
6533.00
1.0000000
1.0000000
1.0000000
1.0000000
6533.00
47
Docket No. RM2019-6 Public Representative Comments
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Number of Observations Read 2745
Number of Observations Used 2745
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 54 5029421 93137 614.56 <.0001
Error 2690 407671 151.55068
Corrected Total 2744 5437092
Root MSE 12.31059 R-Square 0.9250
Dependent Mean 40.24587 Adj R-Sq 0.9235
Coeff Var 30.58846
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
Intercept 1 -33.25287 27.91176 -1.19 0.2336
29.74434 -1.12 0.2637
0
vol 1 0.05138 0.01061 4.84 <.0001
0.02692 1.91 0.0564
968.39657
vol2 1 -0.00000105
2.643277E-7
-3.99 <.0001
7.228669E-7
-1.46 0.1449
7.31424
volcurb 1 0.05028 0.00829 6.07 <.0001
0.01582 3.18 0.0015
78.09108
volcbu 1 0.04397 0.00972 4.52 <.000 0.01759 2.50 0.012 27.38365
48
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
1 5
volcent 1 0.04948 0.00956 5.17 <.0001
0.01847 2.68 0.0074
95.74602
voldoor 1 0.05595 0.00871 6.43 <.0001
0.01684 3.32 0.0009
161.26836
volbr 1 -0.02858 0.01240 -2.30 0.0213
0.03857 -0.74 0.4588
6.80609
voliam 1 -0.03729 0.00867 -4.30 <.0001
0.02979 -1.25 0.2107
121.41778
volfdp 1 -0.03551 0.00849 -4.18 <.0001
0.02823 -1.26 0.2086
175.19862
volifr 1 -0.03699 0.00993 -3.72 0.0002
0.03234 -1.14 0.2528
29.32668
curb 1 12.52110 37.57902 0.33 0.7390
28.92536 0.43 0.6651
1664.63322
cbu 1 -29.98508 46.97732 -0.64 0.5233
39.49984 -0.76 0.4478
812.14759
cent 1 29.61597 41.02145 0.72 0.4704
36.89724 0.80 0.4222
786.63480
door 1 9.68279 35.55722 0.27 0.7854
37.76603 0.26 0.7977
1402.88578
br 1 -197.51518 66.34511 -2.98 0.0029
70.99925 -2.78 0.0054
336.12059
IAM 1 75.91039 59.76133 1.27 0.2041
70.11623 1.08 0.2791
2802.11752
FDP 1 64.71569 56.32738 1.15 0.2507
74.03992 0.87 0.3822
2940.83979
IFR 1 160.74228 64.98173 2.47 0.0134
86.98541 1.85 0.0647
731.54117
curb2 1 29.75158 27.55627 1.08 0.2804
21.09352 1.41 0.1585
582.70624
cbu2 1 -6.59933 39.57375 -0.17 0.8676
30.45709 -0.22 0.8285
195.99667
49
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
cent2 1 11.92150 29.31427 0.41 0.6843
30.78197 0.39 0.6986
174.48754
door2 1 50.38829 24.55560 2.05 0.0403
19.41368 2.60 0.0095
497.49058
br2 1 135.06490 54.75539 2.47 0.0137
58.42210 2.31 0.0209
98.77343
iam2 1 -12.68261 36.48325 -0.35 0.7281
43.45828 -0.29 0.7704
855.40312
fdp2 1 8.11250 32.89312 0.25 0.8052
46.27006 0.18 0.8608
970.90314
ifr2 1 -90.52639 45.82618 -1.98 0.0483
65.64399 -1.38 0.1680
127.16398
curbcbu 1 14.31982 54.88222 0.26 0.7942
42.84050 0.33 0.7382
105.67202
curbcent 1 79.80855 48.16527 1.66 0.0976
44.54139 1.79 0.0733
38.09208
curbdoor
1 71.59393 45.85004 1.56 0.1185
36.65327 1.95 0.0509
160.75237
curbbr 1 44.67102 63.44578 0.70 0.4814
62.52974 0.71 0.4750
60.38884
curbiam 1 -76.83438 37.11454 -2.07 0.0385
29.54968 -2.60 0.0094
283.94556
curbfdp 1 -87.93389 38.73920 -2.27 0.0233
30.59977 -2.87 0.0041
771.53820
curbifr 1 -120.91496 41.43879 -2.92 0.0036
35.70252 -3.39 0.0007
15.40479
cbucent 1 50.74788 56.72956 0.89 0.3711
49.65566 1.02 0.3069
25.02003
cbudoor 1 31.76371 54.39559 0.58 0.5593
40.46356 0.78 0.4325
59.24088
cbubr 1 63.19146 80.23987 0.79 0.4310
69.90467 0.90 0.3661
16.35473
cbuiam 1 16.06419 46.09645 0.35 0.727 37.33535 0.43 0.667 42.30004
50
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
5 0
cbufdp 1 2.06794 46.82094 0.04 0.9648
38.07954 0.05 0.9567
479.51703
cbuifr 1 -45.43360 51.69329 -0.88 0.3795
45.11982 -1.01 0.3140
9.45057
centdoor
1 107.01557 44.83839 2.39 0.0171
44.05082 2.43 0.0152
83.90588
centbr 1 -17.79977 68.37103 -0.26 0.7946
84.57161 -0.21 0.8333
22.56376
centiam 1 -80.75279 38.13536 -2.12 0.0343
36.19413 -2.23 0.0258
155.86560
centfdp 1 -93.35501 38.61589 -2.42 0.0157
36.20054 -2.58 0.0100
106.99510
centifr 1 -115.40789 40.24442 -2.87 0.0042
37.36468 -3.09 0.0020
94.12039
doorbr 1 32.26821 66.19999 0.49 0.6260
65.65090 0.49 0.6231
126.01484
dooriam 1 -92.47181 38.98353 -2.37 0.0178
42.09710 -2.20 0.0281
584.15198
doorfdp 1 -106.73828 40.43924 -2.64 0.0084
43.22508 -2.47 0.0136
762.51477
doorifr 1 -116.37409 40.57520 -2.87 0.0042
47.48967 -2.45 0.0143
33.62971
briam 1 201.29488 62.09882 3.24 0.0012
74.58963 2.70 0.0070
27.27753
brfdp 1 228.83884 63.96195 3.58 0.0004
70.29051 3.26 0.0011
46.95813
brifr 1 97.12951 41.94559 2.32 0.0207
52.15826 1.86 0.0627
10.99859
Iamfdp 1 -2.92510 63.71656 -0.05 0.9634
86.01499 -0.03 0.9729
322.20097
Iamifr 1 -32.78913 75.90970 -0.43 0.6658
100.14286 -0.33 0.7434
68.42406
51
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
fdpifr 1 -17.18126 69.88873 -0.25 0.8058
102.58885 -0.17 0.8670
72.48702
The SAS System
The REG ProcedureModel: MODEL1
Test jtmarallusps Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 425.11184 2.81 <.0001
Denominator 2690 151.55068
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jtmarallusps Results using Heteroscedasticity Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 81.17 <.0001
The SAS System
Obs
m_thours phours elasvol mtvol mcvol mtcurb elascurb
1 40.2459 42.1993 0.90071 0.063291 3.79745 42.714 0.33190
52
Docket No. RM2019-6 Public Representative Comments
Obs
m_thours phours elasvol mtvol mcvol mtcurb elascurb
7
SUNDAY OUTPUT SCALED
The SAS System
The MEANS ProcedureVariable N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
curb
cbu
cent
door
other
br
DE
IAM
FDP
IFR
ODE
vol
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
33.8889909
6.3568816
40.2458725
600.5559199
0.3278959
0.1262009
0.1267505
0.3549808
0.0641718
0.0563836
600.5559199
0.3105419
0.5419724
0.0690464
0.0784393
600.5526412
36.6422673
9.9703193
44.5134535
689.1818796
0.2551530
0.1425660
0.1606803
0.2475542
0.0809493
0.0649420
689.1818796
0.2081660
0.2262575
0.0978172
0.0827383
689.1775982
0.1200000
0
0.1200000
1.0000000
0
0
0
0
-1.11022E-16
0
1.0000000
0
0
0
-8.32667E-17
1.0000000
345.9200000
122.4600000
404.7800000
6533.00
1.0000000
0.9382716
1.0000000
1.0000000
0.8000000
1.0000000
6533.00
1.0000000
1.0000000
1.0000000
1.0000000
6533.00
The SAS System
The REG Procedure
53
Docket No. RM2019-6 Public Representative Comments
Model: MODEL1Dependent Variable: thours
Number of Observations Read 2745
Number of Observations Used 2745
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 54 2538.25581 47.00474 614.56 <.0001
Error 2690 205.74419 0.07648
Corrected Total 2744 2744.00000
Root MSE 0.27656 R-Square 0.9250
Dependent Mean 1.95492E-15 Adj R-Sq 0.9235
Coeff Var 1.414679E16
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
Intercept 1 4.2874E-15 0.00528 0.00 1.0000 0.00523 0.00 1.0000 0
vol 1 0.79549 0.16429 4.84 <.0001 0.41674 1.91 0.0564 968.39657
vol2 1 -0.05694 0.01428 -3.99 <.0001 0.03905 -1.46 0.1449 7.31424
volcurb 1 0.28309 0.04665 6.07 <.0001 0.08907 3.18 0.0015 78.09108
volcbu 1 0.12496 0.02763 4.52 <.0001 0.04997 2.50 0.0125 27.38365
volcent 1 0.26725 0.05166 5.17 <.0001 0.09976 2.68 0.0074 95.74602
voldoor 1 0.43083 0.06705 6.43 <.0001 0.12968 3.32 0.0009 161.26836
volbr 1 -0.03174 0.01377 -2.30 0.0213 0.04283 -0.74 0.4588 6.80609
voliam 1 -0.25018 0.05818 -4.30 <.0001 0.19984 -1.25 0.2107 121.41778
volfdp 1 -0.29235 0.06988 -4.18 <.0001 0.23246 -1.26 0.2086 175.19862
volifr 1 -0.10648 0.02859 -3.72 0.0002 0.09310 -1.14 0.2528 29.32668
curb 1 0.07177 0.21540 0.33 0.7390 0.16580 0.43 0.6651 1664.63322
cbu 1 -0.09604 0.15046 -0.64 0.5233 0.12651 -0.76 0.4478 812.14759
54
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
cent 1 0.10690 0.14808 0.72 0.4704 0.13319 0.80 0.4222 786.63480
door 1 0.05385 0.19775 0.27 0.7854 0.21003 0.26 0.7977 1402.88578
br 1 -0.28816 0.09679 -2.98 0.0029 0.10358 -2.78 0.0054 336.12059
IAM 1 0.35499 0.27947 1.27 0.2041 0.32790 1.08 0.2791 2802.11751
FDP 1 0.32894 0.28631 1.15 0.2507 0.37634 0.87 0.3822 2940.83979
IFR 1 0.35323 0.14280 2.47 0.0134 0.19115 1.85 0.0647 731.54117
curb2 1 0.13760 0.12744 1.08 0.2804 0.09755 1.41 0.1585 582.70624
cbu2 1 -0.01233 0.07391 -0.17 0.8676 0.05689 -0.22 0.8285 195.99667
cent2 1 0.02836 0.06974 0.41 0.6843 0.07323 0.39 0.6986 174.48754
door2 1 0.24164 0.11776 2.05 0.0403 0.09310 2.60 0.0095 497.49058
br2 1 0.12943 0.05247 2.47 0.0137 0.05598 2.31 0.0209 98.77343
iam2 1 -0.05368 0.15441 -0.35 0.7281 0.18393 -0.29 0.7704 855.40312
fdp2 1 0.04057 0.16451 0.25 0.8052 0.23141 0.18 0.8608 970.90314
ifr2 1 -0.11761 0.05954 -1.98 0.0483 0.08528 -1.38 0.1680 127.16398
curbcbu 1 0.01416 0.05427 0.26 0.7942 0.04236 0.33 0.7382 105.67202
curbcent 1 0.05399 0.03258 1.66 0.0976 0.03013 1.79 0.0733 38.09208
curbdoor
1 0.10452 0.06694 1.56 0.1185 0.05351 1.95 0.0509 160.75237
curbbr 1 0.02889 0.04103 0.70 0.4814 0.04044 0.71 0.4750 60.38884
curbiam 1 -0.18417 0.08896 -2.07 0.0385 0.07083 -2.60 0.0094 283.94556
curbfdp 1 -0.33287 0.14665 -2.27 0.0233 0.11584 -2.87 0.0041 771.53820
curbifr 1 -0.06046 0.02072 -2.92 0.0036 0.01785 -3.39 0.0007 15.40479
cbucent 1 0.02362 0.02641 0.89 0.3711 0.02312 1.02 0.3069 25.02003
cbudoor 1 0.02373 0.04064 0.58 0.5593 0.03023 0.78 0.4325 59.24088
cbubr 1 0.01681 0.02135 0.79 0.4310 0.01860 0.90 0.3661 16.35473
cbuiam 1 0.01197 0.03434 0.35 0.7275 0.02781 0.43 0.6670 42.30004
cbufdp 1 0.00511 0.11561 0.04 0.9648 0.09403 0.05 0.9567 479.51703
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Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
cbuifr 1 -0.01426 0.01623 -0.88 0.3795 0.01417 -1.01 0.3140 9.45057
centdoor 1 0.11542 0.04836 2.39 0.0171 0.04751 2.43 0.0152 83.90588
centbr 1 -0.00653 0.02508 -0.26 0.7946 0.03102 -0.21 0.8333 22.56376
centiam 1 -0.13957 0.06591 -2.12 0.0343 0.06256 -2.23 0.0258 155.86560
centfdp 1 -0.13202 0.05461 -2.42 0.0157 0.05119 -2.58 0.0100 106.99510
centifr 1 -0.14688 0.05122 -2.87 0.0042 0.04755 -3.09 0.0020 94.12039
doorbr 1 0.02889 0.05927 0.49 0.6260 0.05877 0.49 0.6231 126.01484
dooriam 1 -0.30268 0.12760 -2.37 0.0178 0.13779 -2.20 0.0281 584.15198
doorfdp 1 -0.38480 0.14579 -2.64 0.0084 0.15583 -2.47 0.0136 762.51477
doorifr 1 -0.08781 0.03062 -2.87 0.0042 0.03583 -2.45 0.0143 33.62971
briam 1 0.08938 0.02757 3.24 0.0012 0.03312 2.70 0.0070 27.27753
brfdp 1 0.12944 0.03618 3.58 0.0004 0.03976 3.26 0.0011 46.95813
brifr 1 0.04054 0.01751 2.32 0.0207 0.02177 1.86 0.0627 10.99859
Iamfdp 1 -0.00435 0.09477 -0.05 0.9634 0.12793 -0.03 0.9729 322.20097
Iamifr 1 -0.01886 0.04367 -0.43 0.6658 0.05761 -0.33 0.7434 68.42406
fdpifr 1 -0.01105 0.04495 -0.25 0.8058 0.06598 -0.17 0.8670 72.48702
The SAS System
The REG ProcedureModel: MODEL1
Test jtmarallusps Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 0.21455 2.81 <.0001
Denominator 2690 0.07648
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Docket No. RM2019-6 Public Representative Comments
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jtmarallusps Results using Heteroscedasticity Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 81.17 <.0001
The SAS System
The REG ProcedureModel: MODEL1
Test jttest Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 198.78604 2599.03 <.0001
Denominator 2690 0.07648
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jttest Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
10 9364.67 <.0001
The SAS System
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Docket No. RM2019-6 Public Representative Comments
Obs m_thours phours elasvol mtvol mcvol mtcurb elascurb
1 40.2459 -20029.04 2.02538 -67.5483
-4052.90 170.081 -.002784395
REGULAR DELIVERY LOG FULL QUADRATIC DECEMBERJT SIGNIFICANCE ERROR AFTER LINE 330
NOTE: Copyright (c) 2002-2012 by SAS Institute Inc., Cary, NC, USA.NOTE: SAS (r) Proprietary Software 9.4 (TS1M0) Licensed to POSTAL REGULATORY COMMISSION, Site 70021410.NOTE: This session is executing on the X64_7PRO platform.
NOTE: Updated analytical products:
SAS/STAT 12.3 (maintenance) SAS/ETS 12.3 (maintenance)
NOTE: Additional host information:
X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation
NOTE: SAS initialization used: real time 3.30 seconds cpu time 1.60 seconds
1 libname SPR1 ! 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.1 ! Public Files\Public Folder\Directory 1 Analysis Data Sets';NOTE: Libref SPR was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1. Public Files\Public Folder\Directory 1 Analysis Data Sets234 ***********************************************************4 ! ***********************************5 ****** The End of THis Program Shows Extreme5 ! MultiCollinearity Among the Variables of ***6 ****** of Interest for the Regular Delivery, December6 ! Variability Calculation ******7 ***********************************************************7 ! ***********************************'8
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9 options nodate;10 ods graphics off;1112 *** Reading in the four regression data sets ***;1314 ***** DECEMBER****************;1516 data sepreg1; set spr.sepreg5;
NOTE: There were 34337 observations read from the data set SPR.SEPREG5.NOTE: The data set WORK.SEPREG1 has 34337 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.25 seconds cpu time 0.04 seconds
17 data decreg1; set spr.decreg5;
NOTE: There were 48627 observations read from the data set SPR.DECREG5.NOTE: The data set WORK.DECREG1 has 48627 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.30 seconds cpu time 0.03 seconds
18 data junreg1; set spr.junreg5;
NOTE: There were 35154 observations read from the data set SPR.JUNREG5.NOTE: The data set WORK.JUNREG1 has 35154 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.23 seconds cpu time 0.06 seconds
19 data marreg1; set spr.marreg5;202122 **** Selecting the month to be estimated ***;
NOTE: There were 33151 observations read from the data set SPR.MARREG5.NOTE: The data set WORK.MARREG1 has 33151 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.22 seconds
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cpu time 0.07 seconds
23 data decreg2;24 set decreg1;252627 **** Selecting the regular delivery observations ***;28 **** Selectin the SPR locations size ******;29 **** Creating a month index *******;30
NOTE: There were 48627 observations read from the data set WORK.DECREG1.NOTE: The data set WORK.DECREG2 has 48627 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.04 seconds
31 data decreg3; set decreg2;32 if ldc = '2300' or ldc = '2400';33 if s_thours ge 96;34 if DEC = '.' then dec = 0;35 if SEP = '.' then sep = 0;36 if JUN = '.' then jun = 0;37 if MAR = '.' then MAR = 0;38 if vol > 0;394041 *** Converting the characteristic variables to levels41 ! ************;42 *** for calculating ratios for combined LDC23 and 2442 ! observations *****;43
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 34:10NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 35:25 36:25 37:25NOTE: There were 48627 observations read from the data set WORK.DECREG2.NOTE: The data set WORK.DECREG3 has 16366 observations and 30 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.06 seconds
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Docket No. RM2019-6 Public Representative Comments
44 data decreg3; set decreg3;45 cbuL = cbu * DT ;46 centL = cent * DT ;47 curbL = curb * DT ;48 doorL = door * DT ;49 otherL = other * DT ;5051 FDPl = FDP * DE ;52 IAMl = IAM * DE ;53 IFRl = IFR * DE ;54 ODEl = ODE * DE ;55 BRL = BR * DT ;565758 **** Cumulating data by finane number / date ****;
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3 has 16366 observations and 40 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
59 proc sort data=decreg3; by fin_no date;60
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3 has 16366 observations and 40 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
61 proc summary data=decreg3; by fin_no date;62 Var63 cbuL64 centL65 curbL66 doorL67 otherL68 FDPL69 IAML70 IFRL71 ODEL72 BRL73 DT
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74 DE75 hours76 ndhours77 thours78 vol79 boxes;80 output out=decreg3a sum=81 cbuL82 centL83 curbL84 doorL85 otherL86 FDPl87 IAMl88 IFRl89 ODEl90 brL91 DT92 DE93 hours94 ndhours95 thours96 vol97 s_boxes98 mean=99 a_cbuL100 a_centL101 a_curbL102 a_doorL103 a_otherL104 a_FDPL105 a_IAML106 a_IFRL107 a_ODEL108 a_BRL109 a_DT110 a_DE111 a_hours112 a_ndhours113 a_thours114 a_vol115 boxes;116117 ;118119 *** Calculating characteristic variabless for combined119! LDCs ***;
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3A has 15904 observations and 38
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variables.NOTE: PROCEDURE SUMMARY used (Total process time): real time 0.27 seconds cpu time 0.25 seconds
120 data decreg3a; set decreg3a;121 cbu = cbuL / DT ;122 cent = centL / DT ;123 curb = curbL / DT ;124 door = doorL / DT ;125 other = otherL / DT ;126127 FDP = FDPL / DE ;128 IAM = IAML / DE ;129 IFR = IFRL / DE ;130 ODE = ODEL / DE ;131 br = BRL / DT ;132
NOTE: There were 15904 observations read from the data set WORK.DECREG3A.NOTE: The data set WORK.DECREG3A has 15904 observations and 48 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
133 DATA decreg3B; set decreg3a;134 keep fin_no date135 cbu136 cent137 curb138 door139 other140 FDP141 IAM142 IFR143 ODE144 br145 DT146 hours147 ndhours148 thours149 vol150 boxes;151152 run;
NOTE: There were 15904 observations read from the data set
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Docket No. RM2019-6 Public Representative Comments
WORK.DECREG3A.NOTE: The data set WORK.DECREG3B has 15904 observations and 18 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
153154 *** Constructing variables needed for econometric equation154! ***;155156 data decreg3C; set decreg3B;157 cv=boxes;158 cv2 = cv * cv;159 vol2=vol*vol;160 curb2 = curb * curb ;161 cbu2 = cbu * cbu ;162 cent2 = cent * cent ;163 door2 = door * door ;164 br2 = br * br ;165 iam2 = iam * iam ;166 fdp2 = fdp * fdp ;167 ifr2 = ifr * ifr ;168169 volcurb=vol*curb;170 volcbu=vol*cbu;171 volcent=vol*cent;172 voldoor=vol*door;173 volbr=vol*br;174 voliam=vol*iam;175 volfdp=vol*fdp;176 volifr=vol*ifr;177 volode=vol*ode;178 volcv=vol*cv;179180 cvcurb=cv*curb;181 cvcbu=cv*cbu;182 cvcent=cv*cent;183 cvdoor=cv*door;184 cvbr=cv*br;185 cviam=cv*iam;186 cvfdp=cv*fdp;187 cvifr=cv*ifr;188 cvode=cv*ode;189190 curbcbu = curb * cbu ;191 curbcent = curb * cent ;192 curbdoor = curb * door ;193 curbbr = curb * br ;194 curbiam = curb * iam ;
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Docket No. RM2019-6 Public Representative Comments
195 curbfdp = curb * fdp ;196 curbifr = curb * ifr ;197198 cbucent = cbu * cent ;199 cbudoor = cbu * door ;200 cbubr = cbu * br ;201 cbuiam = cbu * iam ;202 cbufdp = cbu * fdp ;203 cbuifr = cbu * ifr ;204205 centdoor = cent * door ;206 centbr = cent * br ;207 centiam = cent * iam ;208 centfdp = cent * fdp ;209 centifr = cent * ifr ;210211 doorbr = door * br ;212 dooriam = door * iam ;213 doorfdp = door * fdp ;214 doorifr = door * ifr ;215216 briam = br * iam ;217 brfdp = br * fdp ;218 brifr = br * ifr ;219220 Iamfdp = Iam * fdp ;221 Iamifr = Iam * ifr ;222223 fdpifr = fdp * ifr ;224
NOTE: There were 15904 observations read from the data set WORK.DECREG3B.NOTE: The data set WORK.DECREG3C has 15904 observations and 76 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.06 seconds
225 data decreg4; set decreg3C;226227228 *** Producing means required for variability calculation228! ***;
NOTE: There were 15904 observations read from the data set WORK.DECREG3C.NOTE: The data set WORK.DECREG4 has 15904 observations and 76 variables.NOTE: DATA statement used (Total process time):
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real time 0.03 seconds cpu time 0.03 seconds
229 proc means ; var230 hours231 ndhours232 thours233 DT234 curb235 cbu236 cent237 door238 other239 br240 IAM241 FDP242 IFR243 ODE244 vol245 cv246 boxes247 ;248249 output out=regmean mean=250 m_hours251 m_ndhours252 m_thours253 m_DT254 m_curb255 m_cbu256 m_cent257 m_door258 m_other259 m_br260 m_IAM261 m_FDP262 m_IFR263 m_ODE264 m_vol265 m_cv266 m_boxes267 ;268269270 run;
NOTE: Writing HTML Body file: sashtml.htmNOTE: There were 15904 observations read from the data set WORK.DECREG4.NOTE: The data set WORK.REGMEAN has 1 observations and 19
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variables.NOTE: PROCEDURE MEANS used (Total process time): real time 0.61 seconds cpu time 0.48 seconds
271272 **** Estimating the econometric model *****;273274 proc reg data=decreg4 outest=quadc;275 model thours=vol vol2 volcurb volcbu volcent voldoor volbr275! voliam volfdp volifr volcv276 cv cv2 cvcurb cvcbu cvcent cvdoor cvbr276! cviam cvfdp cvifr277 curb cbu cent door br iam fdp ifr curb2277! cbu2 cent2 door2 br2 iam2 fdp2 ifr2278279280 curbcbu281 curbcent282 curbdoor283 curbbr284 curbiam285 curbfdp286 curbifr287 cbucent288 cbudoor289 cbubr290 cbuiam291 cbufdp292 cbuifr293 centdoor294 centbr295 centiam296 centfdp297 centifr298 doorbr299 dooriam300 doorfdp301 doorifr302 briam303 brfdp304 brifr305 Iamfdp306 Iamifr307 fdpifr308309310311 /vif white ;312
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313 **************************;314 **** TEST SECTION ****;315 **************************;316317318 f1: test curbcbu, curbcent, curbdoor, curbbr,318! curbiam, curbfdp, curbifr, cbucent, cbudoor,319 cbubr, cbuiam, cbufdp, cbuifr, centdoor,319! centbr, centiam, centfdp, centifr, doorbr, dooriam,320 doorfdp, doorifr, briam, brfdp, brifr, Iamfdp,320! Iamifr, fdpifr;321322 f2cv: test cv, cv2, cvcurb, cvcbu, cvcent,322! cvdoor, cvbr, cviam, cvfdp, cvifr;323324 f3jtvol: test vol, vol2, volcv, volcurb, volcbu,324! volcent, voldoor,volbr, voliam,325 volfdp, volifr;326327328329 **** Calculating variabilities and marginal times ****;330
ERROR: The TEST is not consistent or has redundant column.NOTE: The data set WORK.QUADC has 1 observations and 71 variables.NOTE: PROCEDURE REG used (Total process time): real time 1.14 seconds cpu time 1.04 seconds
331 data elasquad; merge regmean quadc(drop=_type_);332333 phours = INTERCEPT334 +vol*m_vol +vol2*m_vol*m_vol+cv*m_cv+cv2*m_cv*m_cv335 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_v335! ol*m_cent+voldoor*m_vol*m_door+volbr*m_vol*m_br +335! voliam*m_vol*m_iam + volfdp*m_vol*m_fdp335! +volifr*m_vol*m_ifr+volcv*m_vol*m_cv336 +cvcurb*m_cv*m_curb +336! cvcbu*m_cv*m_cbu+cvcent*m_cv*m_cent+cvdoor*m_cv*m_door+cvbr336! *m_cv*m_br + cviam*m_cv*m_iam + cvfdp*m_cv*m_fdp336! +cvifr*m_cv*m_ifr337 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door338 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu338! +cent2*m_cent*m_cent+ door2*m_door*m_door339 + br*m_br+ br2*m_br*m_br340 + iam*m_iam + fdp*m_fdp + ifr*m_ifr +340! iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp + ifr2*m_ifr*m_ifr341 + curbcbu * m_curb * m_cbu
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Docket No. RM2019-6 Public Representative Comments
342 + curbcent * m_curb * m_cent343 + curbdoor * m_curb * m_door344 + curbbr * m_curb * m_br345 + curbiam * m_curb * m_iam346 + curbfdp * m_curb * m_fdp347 + curbifr * m_curb * m_ifr348 + cbucent * m_cbu * m_cent349 + cbudoor * m_cbu * m_door350 + cbubr * m_cbu * m_br351 + cbuiam * m_cbu * m_iam352 + cbufdp * m_cbu * m_fdp353 + cbuifr * m_cbu * m_ifr354 + centdoor * m_cent * m_door355 + centbr * m_cent * m_br356 + centiam * m_cent * m_iam357 + centfdp * m_cent * m_fdp358 + centifr * m_cent * m_ifr359 + doorbr * m_door * m_br360 + dooriam * m_door * m_iam361 + doorfdp * m_door * m_fdp362 + doorifr * m_door * m_ifr363 + briam * m_br * m_iam364 + brfdp * m_br * m_fdp365 + brifr * m_br * m_ifr366 + Iamfdp * m_iam * m_fdp367 + Iamifr * m_iam * m_ifr368 + fdpifr * m_fdp * m_ifr369370 ;371372 elasvol=mtvol*m_vol;373374375 mtvol= vol + 2*vol2*m_vol + volcv*m_cv + volcurb*m_curb +375! volcbu*m_cbu + volcent*m_cent + voldoor*m_door +376 volbr*m_br + voliam*m_iam + volfdp*m_fdp +376! volifr*m_ifr ;377 elasvol=mtvol*m_vol/phours;378 mcvol=60*mtvol;379380381 mtcv= cv + 2*cv2*m_cv + volcv*m_vol + cvcurb*m_curb +381! cvcbu*m_cbu + cvcent*m_cent + cvdoor*m_door +382 cvbr*m_br + cviam*m_iam + cvfdp*m_fdp + cvifr*m_ifr382! ;383 elascv=mtcv*m_cv/phours;384 mccv=60*mtcv;385386
NOTE: Missing values were generated as a result of performing
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Docket No. RM2019-6 Public Representative Comments
an operation on missing values. Each place is given by: (Number of times) at (Line):(Column). 1 at 372:14NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.NOTE: The data set WORK.ELASQUAD has 1 observations and 96 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.04 seconds
387 proc print data=elasquad;388 var m_thours phours elasvol mtvol mcvol elascv mtcv mccv ;389390 run;
NOTE: There were 1 observations read from the data set WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.02 seconds cpu time 0.00 seconds
REGULAR LOG DELIVERY FULL QUADRATIC SCALED
400 libname SPR400! 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.400! Public Files\Public Folder\Directory 1 Analysis Data Sets';NOTE: Libref SPR was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1. Public Files\Public Folder\Directory 1 Analysis Data Sets401402403 ***********************************************************403! ***********************************404 ****** The End of THis Program Shows Extreme404! MultiCollinearity Among the Variables of ***405 ****** of Interest for the Regular Delivery, December405! Variability Calculation ******406 ***********************************************************406! ***********************************'407408 options nodate;409 ods graphics off;410
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Docket No. RM2019-6 Public Representative Comments
411 *** Reading in the four regression data sets ***;412413 ***** DECEMBER****************;414415 data sepreg1; set spr.sepreg5;
NOTE: There were 34337 observations read from the data set SPR.SEPREG5.NOTE: The data set WORK.SEPREG1 has 34337 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.22 seconds cpu time 0.06 seconds
416 data decreg1; set spr.decreg5;
NOTE: There were 48627 observations read from the data set SPR.DECREG5.NOTE: The data set WORK.DECREG1 has 48627 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.32 seconds cpu time 0.07 seconds
417 data junreg1; set spr.junreg5;
NOTE: There were 35154 observations read from the data set SPR.JUNREG5.NOTE: The data set WORK.JUNREG1 has 35154 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.21 seconds cpu time 0.06 seconds
418 data marreg1; set spr.marreg5;419420421 **** Selecting the month to be estimated ***;
NOTE: There were 33151 observations read from the data set SPR.MARREG5.NOTE: The data set WORK.MARREG1 has 33151 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.21 seconds cpu time 0.09 seconds
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Docket No. RM2019-6 Public Representative Comments
422 data decreg2;423 set decreg1;424425426 **** Selecting the regular delivery observations ***;427 **** Selectin the SPR locations size ******;428 **** Creating a month index *******;429
NOTE: There were 48627 observations read from the data set WORK.DECREG1.NOTE: The data set WORK.DECREG2 has 48627 observations and 27 variables.NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.03 seconds
430 data decreg3; set decreg2;431 if ldc = '2300' or ldc = '2400';432 if s_thours ge 96;433 if DEC = '.' then dec = 0;434 if SEP = '.' then sep = 0;435 if JUN = '.' then jun = 0;436 if MAR = '.' then MAR = 0;437 if vol > 0;438439440 *** Converting the characteristic variables to levels440! ************;441 *** for calculating ratios for combined LDC23 and 24441! observations *****;442
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 433:10NOTE: Numeric values have been converted to character values at the places given by: (Line):(Column). 434:25 435:25 436:25NOTE: There were 48627 observations read from the data set WORK.DECREG2.NOTE: The data set WORK.DECREG3 has 16366 observations and 30 variables.NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.04 seconds
443 data decreg3; set decreg3;444 cbuL = cbu * DT ;
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Docket No. RM2019-6 Public Representative Comments
445 centL = cent * DT ;446 curbL = curb * DT ;447 doorL = door * DT ;448 otherL = other * DT ;449450 FDPl = FDP * DE ;451 IAMl = IAM * DE ;452 IFRl = IFR * DE ;453 ODEl = ODE * DE ;454 BRL = BR * DT ;455456457 **** Cumulating data by finane number / date ****;
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3 has 16366 observations and 40 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.03 seconds
458 proc sort data=decreg3; by fin_no date;459
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3 has 16366 observations and 40 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.04 seconds cpu time 0.04 seconds
460 proc summary data=decreg3; by fin_no date;461 Var462 cbuL463 centL464 curbL465 doorL466 otherL467 FDPL468 IAML469 IFRL470 ODEL471 BRL472 DT473 DE474 hours475 ndhours
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Docket No. RM2019-6 Public Representative Comments
476 thours477 vol478 boxes;479 output out=decreg3a sum=480 cbuL481 centL482 curbL483 doorL484 otherL485 FDPl486 IAMl487 IFRl488 ODEl489 brL490 DT491 DE492 hours493 ndhours494 thours495 vol496 s_boxes497 mean=498 a_cbuL499 a_centL500 a_curbL501 a_doorL502 a_otherL503 a_FDPL504 a_IAML505 a_IFRL506 a_ODEL507 a_BRL508 a_DT509 a_DE510 a_hours511 a_ndhours512 a_thours513 a_vol514 boxes;515516 ;517518 *** Calculating characteristic variabless for combined518! LDCs ***;
NOTE: There were 16366 observations read from the data set WORK.DECREG3.NOTE: The data set WORK.DECREG3A has 15904 observations and 38 variables.NOTE: PROCEDURE SUMMARY used (Total process time): real time 0.26 seconds
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Docket No. RM2019-6 Public Representative Comments
cpu time 0.26 seconds
519 data decreg3a; set decreg3a;520 cbu = cbuL / DT ;521 cent = centL / DT ;522 curb = curbL / DT ;523 door = doorL / DT ;524 other = otherL / DT ;525526 FDP = FDPL / DE ;527 IAM = IAML / DE ;528 IFR = IFRL / DE ;529 ODE = ODEL / DE ;530 br = BRL / DT ;531
NOTE: There were 15904 observations read from the data set WORK.DECREG3A.NOTE: The data set WORK.DECREG3A has 15904 observations and 48 variables.NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.04 seconds
532 DATA decreg3B; set decreg3a;533 keep fin_no date534 cbu535 cent536 curb537 door538 other539 FDP540 IAM541 IFR542 ODE543 br544 DT545 hours546 ndhours547 thours548 vol549 boxes;550551 run;
NOTE: There were 15904 observations read from the data set WORK.DECREG3A.NOTE: The data set WORK.DECREG3B has 15904 observations and 18 variables.
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Docket No. RM2019-6 Public Representative Comments
NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.01 seconds
552553 *** Constructing variables needed for econometric equation553! ***;554555 data decreg3C; set decreg3B;556 cv=boxes;557 cv2 = cv * cv;558 vol2=vol*vol;559 curb2 = curb * curb ;560 cbu2 = cbu * cbu ;561 cent2 = cent * cent ;562 door2 = door * door ;563 br2 = br * br ;564 iam2 = iam * iam ;565 fdp2 = fdp * fdp ;566 ifr2 = ifr * ifr ;567568 volcurb=vol*curb;569 volcbu=vol*cbu;570 volcent=vol*cent;571 voldoor=vol*door;572 volbr=vol*br;573 voliam=vol*iam;574 volfdp=vol*fdp;575 volifr=vol*ifr;576 volode=vol*ode;577 volcv=vol*cv;578579 cvcurb=cv*curb;580 cvcbu=cv*cbu;581 cvcent=cv*cent;582 cvdoor=cv*door;583 cvbr=cv*br;584 cviam=cv*iam;585 cvfdp=cv*fdp;586 cvifr=cv*ifr;587 cvode=cv*ode;588589 curbcbu = curb * cbu ;590 curbcent = curb * cent ;591 curbdoor = curb * door ;592 curbbr = curb * br ;593 curbiam = curb * iam ;594 curbfdp = curb * fdp ;595 curbifr = curb * ifr ;596
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Docket No. RM2019-6 Public Representative Comments
597 cbucent = cbu * cent ;598 cbudoor = cbu * door ;599 cbubr = cbu * br ;600 cbuiam = cbu * iam ;601 cbufdp = cbu * fdp ;602 cbuifr = cbu * ifr ;603604 centdoor = cent * door ;605 centbr = cent * br ;606 centiam = cent * iam ;607 centfdp = cent * fdp ;608 centifr = cent * ifr ;609610 doorbr = door * br ;611 dooriam = door * iam ;612 doorfdp = door * fdp ;613 doorifr = door * ifr ;614615 briam = br * iam ;616 brfdp = br * fdp ;617 brifr = br * ifr ;618619 Iamfdp = Iam * fdp ;620 Iamifr = Iam * ifr ;621622 fdpifr = fdp * ifr ;623624625 ***************************Scaling Data to Correct Test625! Error *****************;626
NOTE: There were 15904 observations read from the data set WORK.DECREG3B.NOTE: The data set WORK.DECREG3C has 15904 observations and 76 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.06 seconds
627 data decreg4; set decreg3C;
NOTE: There were 15904 observations read from the data set WORK.DECREG3C.NOTE: The data set WORK.DECREG4 has 15904 observations and 76 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
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Docket No. RM2019-6 Public Representative Comments
628 proc stdize data=decreg4 out=decreg4new;629630631 *** Producing means required for variability calculation631! ***;632
NOTE: No VAR statement is given. All numerical variables not named elsewhere make up the first set of variables.NOTE: There were 15904 observations read from the data set WORK.DECREG4.NOTE: The data set WORK.DECREG4NEW has 15904 observations and 76 variables.NOTE: PROCEDURE STDIZE used (Total process time): real time 0.09 seconds cpu time 0.07 seconds
633 data decreg4new;set decreg4new;
NOTE: There were 15904 observations read from the data set WORK.DECREG4NEW.NOTE: The data set WORK.DECREG4NEW has 15904 observations and 76 variables.NOTE: DATA statement used (Total process time): real time 0.04 seconds cpu time 0.03 seconds
634 proc means ; var635 hours636 ndhours637 thours638 DT639 curb640 cbu641 cent642 door643 other644 br645 IAM646 FDP647 IFR648 ODE649 vol650 cv651 boxes652 ;653654 output out=regmean mean=
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Docket No. RM2019-6 Public Representative Comments
655 m_hours656 m_ndhours657 m_thours658 m_DT659 m_curb660 m_cbu661 m_cent662 m_door663 m_other664 m_br665 m_IAM666 m_FDP667 m_IFR668 m_ODE669 m_vol670 m_cv671 m_boxes672 ;673674675 run;
NOTE: There were 15904 observations read from the data set WORK.DECREG4NEW.NOTE: The data set WORK.REGMEAN has 1 observations and 19 variables.NOTE: PROCEDURE MEANS used (Total process time): real time 0.22 seconds cpu time 0.18 seconds
676677 **** Estimating the econometric model *****;678679 proc reg data=decreg4new outest=quadc;680 model thours=vol vol2 volcurb volcbu volcent voldoor volbr680! voliam volfdp volifr volcv681 cv cv2 cvcurb cvcbu cvcent cvdoor cvbr681! cviam cvfdp cvifr682 curb cbu cent door br iam fdp ifr curb2682! cbu2 cent2 door2 br2 iam2 fdp2 ifr2683684685 curbcbu686 curbcent687 curbdoor688 curbbr689 curbiam690 curbfdp691 curbifr692 cbucent
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Docket No. RM2019-6 Public Representative Comments
693 cbudoor694 cbubr695 cbuiam696 cbufdp697 cbuifr698 centdoor699 centbr700 centiam701 centfdp702 centifr703 doorbr704 dooriam705 doorfdp706 doorifr707 briam708 brfdp709 brifr710 Iamfdp711 Iamifr712 fdpifr713714715716 /vif white ;717718 **************************;719 **** TEST SECTION ****;720 **************************;721722723 f1: test curbcbu, curbcent, curbdoor, curbbr,723! curbiam, curbfdp, curbifr, cbucent, cbudoor,724 cbubr, cbuiam, cbufdp, cbuifr, centdoor,724! centbr, centiam, centfdp, centifr, doorbr, dooriam,725 doorfdp, doorifr, briam, brfdp, brifr, Iamfdp,725! Iamifr, fdpifr;726727 f2cv: test cv, cv2, cvcurb, cvcbu, cvcent,727! cvdoor, cvbr, cviam, cvfdp, cvifr;728729 f3jtvol: test vol, vol2, volcv, volcurb, volcbu,729! volcent, voldoor,volbr, voliam,730 volfdp, volifr;731732733 run;
734735 **** Calculating variabilities and marginal times ****;736
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Docket No. RM2019-6 Public Representative Comments
NOTE: The data set WORK.QUADC has 1 observations and 71 variables.NOTE: PROCEDURE REG used (Total process time): real time 1.23 seconds cpu time 1.14 seconds
737 data elasquad; merge regmean quadc(drop=_type_);738739 phours = INTERCEPT740 +vol*m_vol +vol2*m_vol*m_vol+cv*m_cv+cv2*m_cv*m_cv741 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_v741! ol*m_cent+voldoor*m_vol*m_door+volbr*m_vol*m_br +741! voliam*m_vol*m_iam + volfdp*m_vol*m_fdp741! +volifr*m_vol*m_ifr+volcv*m_vol*m_cv742 +cvcurb*m_cv*m_curb +742! cvcbu*m_cv*m_cbu+cvcent*m_cv*m_cent+cvdoor*m_cv*m_door+cvbr742! *m_cv*m_br + cviam*m_cv*m_iam + cvfdp*m_cv*m_fdp742! +cvifr*m_cv*m_ifr743 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door744 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu744! +cent2*m_cent*m_cent+ door2*m_door*m_door745 + br*m_br+ br2*m_br*m_br746 + iam*m_iam + fdp*m_fdp + ifr*m_ifr +746! iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp + ifr2*m_ifr*m_ifr747 + curbcbu * m_curb * m_cbu748 + curbcent * m_curb * m_cent749 + curbdoor * m_curb * m_door750 + curbbr * m_curb * m_br751 + curbiam * m_curb * m_iam752 + curbfdp * m_curb * m_fdp753 + curbifr * m_curb * m_ifr754 + cbucent * m_cbu * m_cent755 + cbudoor * m_cbu * m_door756 + cbubr * m_cbu * m_br757 + cbuiam * m_cbu * m_iam758 + cbufdp * m_cbu * m_fdp759 + cbuifr * m_cbu * m_ifr760 + centdoor * m_cent * m_door761 + centbr * m_cent * m_br762 + centiam * m_cent * m_iam763 + centfdp * m_cent * m_fdp764 + centifr * m_cent * m_ifr765 + doorbr * m_door * m_br766 + dooriam * m_door * m_iam767 + doorfdp * m_door * m_fdp768 + doorifr * m_door * m_ifr769 + briam * m_br * m_iam770 + brfdp * m_br * m_fdp771 + brifr * m_br * m_ifr772 + Iamfdp * m_iam * m_fdp
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Docket No. RM2019-6 Public Representative Comments
773 + Iamifr * m_iam * m_ifr774 + fdpifr * m_fdp * m_ifr775776 ;777778 elasvol=mtvol*m_vol;779780781 mtvol= vol + 2*vol2*m_vol + volcv*m_cv + volcurb*m_curb +781! volcbu*m_cbu + volcent*m_cent + voldoor*m_door +782 volbr*m_br + voliam*m_iam + volfdp*m_fdp +782! volifr*m_ifr ;783 elasvol=mtvol*m_vol/phours;784 mcvol=60*mtvol;785786787 mtcv= cv + 2*cv2*m_cv + volcv*m_vol + cvcurb*m_curb +787! cvcbu*m_cbu + cvcent*m_cent + cvdoor*m_door +788 cvbr*m_br + cviam*m_iam + cvfdp*m_fdp + cvifr*m_ifr788! ;789 elascv=mtcv*m_cv/phours;790 mccv=60*mtcv;791792
NOTE: Missing values were generated as a result of performing an operation on missing values. Each place is given by: (Number of times) at (Line):(Column). 1 at 778:14NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.NOTE: The data set WORK.ELASQUAD has 1 observations and 96 variables.NOTE: DATA statement used (Total process time): real time 0.12 seconds cpu time 0.07 seconds
793 proc print data=elasquad;794 var m_thours phours elasvol mtvol mcvol elascv mtcv mccv ;795796 run;
NOTE: There were 1 observations read from the data set WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
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Docket No. RM2019-6 Public Representative Comments
COLLECTION LOG FULL QUADRATIC SHOWING JOINT SIGNIFICANCE ERROR AFTER LINE 2281
1914 Libname SUN 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.Public Files\Public1914! Folder\Directory 1 Analysis Data Sets';NOTE: Libref SUN refers to the same physical library as COL.NOTE: Libref SUN was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.Public Files\Public Folder\Directory 1 Analysis Data Sets191519161917 options nodate;1918 ods graphics off;19191920 ****************************************************;1921 **** Read in Sunday Data ************************;1922 ****************************************************;192319241925 DATA Jun_sunreg1; set sun.June_SUNANA;1926
NOTE: There were 2460 observations read from the data set SUN.JUNE_SUNANA.NOTE: The data set WORK.JUN_SUNREG1 has 2460 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.00 seconds
1927 DATA Sep_sunreg1; set SUN.sep_SUNANA;1928
NOTE: There were 2628 observations read from the data set SUN.SEP_SUNANA.NOTE: The data set WORK.SEP_SUNREG1 has 2628 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
1929 DATA Dec_sunreg1; set SUN.dec_SUNANA;1930
NOTE: There were 8420 observations read from the data set SUN.DEC_SUNANA.NOTE: The data set WORK.DEC_SUNREG1 has 8420 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.32 seconds cpu time 0.01 seconds
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Docket No. RM2019-6 Public Representative Comments
1931 data Mar_sunreg1; set sun.mar_sunana;19321933 **** Selecting the month to be estimated ***;1934
NOTE: There were 3036 observations read from the data set SUN.MAR_SUNANA.NOTE: The data set WORK.MAR_SUNREG1 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
1935 data sunreg2; set mar_sunreg1 ;193619371938 **** Selecting the Sunday delivery observations ***;1939
NOTE: There were 3036 observations read from the data set WORK.MAR_SUNREG1.NOTE: The data set WORK.SUNREG2 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
1940 data sunreg3; set sunreg2;1941 if hours = '.' then delete;1942 if ldc = '2400';19431944 /*if ldc = '2300' or ldc = '2400';*/1945 if vol > 0;194619471948 *** Converting the characteristic variables to levels ************;1949 *** for calculating ratios for combined LDC23 and 24 observations *****;1950
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 1941:12NOTE: There were 3036 observations read from the data set WORK.SUNREG2.NOTE: The data set WORK.SUNREG3 has 2684 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.04 seconds
1951 data sunreg3; set sunreg3;1952 cbuL = cbu * DT ;1953 centL = cent * DT ;
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Docket No. RM2019-6 Public Representative Comments
1954 curbL = curb * DT ;1955 doorL = door * DT ;1956 otherL = other * DT ;19571958 FDPl = FDP * DE ;1959 IAMl = IAM * DE ;1960 IFRl = IFR * DE ;1961 ODEl = ODE * DE ;1962 BRL = BR * DT ;196319641965 **** Cumulating data by finance number / date ****;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
1966 proc sort data=sunreg3; by fin_no date;1967
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.00 seconds cpu time 0.00 seconds
1968 proc summary data=sunreg3; by fin_no date;1969 Var1970 cbuL1971 centL1972 curbL1973 doorL1974 otherL1975 FDPL1976 IAML1977 IFRL1978 ODEL1979 BRL1980 DT1981 DE1982 hours1983 ndhours1984 thours1985 vol1986 ;1987 output out=sunreg3a sum=1988 cbuL
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Docket No. RM2019-6 Public Representative Comments
1989 centL1990 curbL1991 doorL1992 otherL1993 FDPl1994 IAMl1995 IFRl1996 ODEl1997 brL1998 DT1999 DE2000 hours2001 ndhours2002 thours2003 vol20042005 mean=2006 a_cbuL2007 a_centL2008 a_curbL2009 a_doorL2010 a_otherL2011 a_FDPL2012 a_IAML2013 a_IFRL2014 a_ODEL2015 a_BRL2016 a_DT2017 a_DE2018 a_hours2019 a_ndhours2020 a_thours2021 a_vol2022 ;20232024 ;20252026 *** Calculating characteristic variables for combined LDCs ***;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3A has 2684 observations and 36 variables.NOTE: PROCEDURE SUMMARY used (Total process time): real time 0.09 seconds cpu time 0.09 seconds
2027 data sunreg3a; set sunreg3a;2028 cbu = cbuL / DT ;2029 cent = centL / DT ;2030 curb = curbL / DT ;2031 door = doorL / DT ;
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Docket No. RM2019-6 Public Representative Comments
2032 other = otherL / DT ;20332034 FDP = FDPL / DE ;2035 IAM = IAML / DE ;2036 IFR = IFRL / DE ;2037 ODE = ODEL / DE ;2038 br = BRL / DT ;2039
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3A has 2684 observations and 46 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
2040 DATA sunreg3B; set sunreg3a;2041 keep fin_no date2042 cbu2043 cent2044 curb2045 door2046 other2047 FDP2048 IAM2049 IFR2050 ODE2051 br2052 DT2053 DE2054 hours2055 ndhours2056 thours2057 vol2058 ;20592060 run;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3B has 2684 observations and 18 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
20612062 *** Constructing variables needed for econometric equation ***;2063206420652066
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Docket No. RM2019-6 Public Representative Comments
2067 data sunreg3C; set sunreg3b;2068 vol2=vol*vol;2069 curb2 = curb * curb ;2070 cbu2 = cbu * cbu ;2071 cent2 = cent * cent ;2072 door2 = door * door ;2073 br2 = br * br ;2074 iam2 = iam * iam ;2075 fdp2 = fdp * fdp ;2076 ifr2 = ifr * ifr ;20772078 volcurb=vol*curb;2079 volcbu=vol*cbu;2080 volcent=vol*cent;2081 voldoor=vol*door;2082 volbr=vol*br;2083 voliam=vol*iam;2084 volfdp=vol*fdp;2085 volifr=vol*ifr;2086 volode=vol*ode;2087 curbcbu = curb * cbu ;2088 curbcent = curb * cent ;2089 curbdoor = curb * door ;2090 curbbr = curb * br ;2091 curbiam = curb * iam ;2092 curbfdp = curb * fdp ;2093 curbifr = curb * ifr ;20942095 cbucent = cbu * cent ;2096 cbudoor = cbu * door ;2097 cbubr = cbu * br ;2098 cbuiam = cbu * iam ;2099 cbufdp = cbu * fdp ;2100 cbuifr = cbu * ifr ;21012102 centdoor = cent * door ;2103 centbr = cent * br ;2104 centiam = cent * iam ;2105 centfdp = cent * fdp ;2106 centifr = cent * ifr ;21072108 doorbr = door * br ;2109 dooriam = door * iam ;2110 doorfdp = door * fdp ;2111 doorifr = door * ifr ;21122113 briam = br * iam ;2114 brfdp = br * fdp ;2115 brifr = br * ifr ;21162117 Iamfdp = Iam * fdp ;
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Docket No. RM2019-6 Public Representative Comments
2118 Iamifr = Iam * ifr ;21192120 fdpifr = fdp * ifr ;21212122
NOTE: There were 2684 observations read from the data set WORK.SUNREG3B.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.04 seconds
2123 proc means; var21242125 hours2126 ndhours2127 thours2128 DT2129 curb2130 cbu2131 cent2132 door2133 other2134 br2135 DE2136 IAM2137 FDP2138 IFR2139 ODE2140 vol2141 ;21422143 output out=regmean mean=2144 m_hours2145 m_ndhours2146 m_thours2147 m_DT2148 m_curb2149 m_cbu2150 m_cent2151 m_door2152 m_other2153 m_br2154 m_DE2155 m_IAM2156 m_FDP2157 m_IFR2158 m_ODE2159 m_vol;2160
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Docket No. RM2019-6 Public Representative Comments
21612162
NOTE: Writing HTML Body file: sashtml10.htmNOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.REGMEAN has 1 observations and 18 variables.NOTE: PROCEDURE MEANS used (Total process time): real time 3.90 seconds cpu time 0.43 seconds
2163 proc reg data=SUNreg3C outest=quadc;2164 model thours=vol vol2 volcurb volcbu volcent voldoor volbr voliam volfdp volifr2165 curb cbu cent door br iam fdp ifr curb2 cbu2 cent2 door2 br2 iam2 fdp2 ifr22166 curbcbu2167 curbcent2168 curbdoor2169 curbbr2170 curbiam2171 curbfdp2172 curbifr2173 cbucent2174 cbudoor2175 cbubr2176 cbuiam2177 cbufdp2178 cbuifr2179 centdoor2180 centbr2181 centiam2182 centfdp2183 centifr2184 doorbr2185 dooriam2186 doorfdp2187 doorifr2188 briam2189 brfdp2190 brifr2191 Iamfdp2192 Iamifr2193 fdpifr2194219521962197 /vif white ;21982199 f1: test curbcbu,2200 curbcent,2201 curbdoor,2202 curbbr,
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Docket No. RM2019-6 Public Representative Comments
2203 curbiam,2204 curbfdp,2205 curbifr,2206 cbucent,2207 cbudoor,2208 cbubr,2209 cbuiam,2210 cbufdp,2211 cbuifr,2212 centdoor,2213 centbr,2214 centiam,2215 centfdp,2216 centifr,2217 doorbr,2218 dooriam,2219 doorfdp,2220 doorifr,2221 briam,2222 brfdp,2223 brifr,2224 Iamfdp,2225 Iamifr,2226 fdpifr;22272228 f3jtvol: test vol, vol2, volcurb, volcbu, volcent, voldoor, volbr, voliam, volfdp, volifr;2228! run;
ERROR: The TEST is not consistent or has redundant column.2229
NOTE: The data set WORK.QUADC has 1 observations and 60 variables.NOTE: PROCEDURE REG used (Total process time): real time 0.46 seconds cpu time 0.39 seconds
2230 data elasquad; merge regmean quadc(drop=_type_);22312232 phours = INTERCEPT2233 +vol*m_vol +vol2*m_vol*m_vol2234 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_vol*m_cent+voldoor*m_vol*m_door+volbr*2234! m_vol*m_br + voliam*m_vol*m_iam + volfdp*m_vol*m_fdp +volifr*m_vol*m_ifr2235 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door2236 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu +cent2*m_cent*m_cent+ door2*m_door*m_door2237 + br*m_br+ br2*m_br*m_br2238 + iam*m_iam + fdp*m_fdp + ifr*m_ifr + iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp +2238! ifr2*m_ifr*m_ifr2239 + curbcbu * m_curb * m_cbu2240 + curbcent * m_curb * m_cent
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Docket No. RM2019-6 Public Representative Comments
2241 + curbdoor * m_curb * m_door2242 + curbbr * m_curb * m_br2243 + curbiam * m_curb * m_iam2244 + curbfdp * m_curb * m_fdp2245 + curbifr * m_curb * m_ifr2246 + cbucent * m_cbu * m_cent2247 + cbudoor * m_cbu * m_door2248 + cbubr * m_cbu * m_br2249 + cbuiam * m_cbu * m_iam2250 + cbufdp * m_cbu * m_fdp2251 + cbuifr * m_cbu * m_ifr2252 + centdoor * m_cent * m_door2253 + centbr * m_cent * m_br2254 + centiam * m_cent * m_iam2255 + centfdp * m_cent * m_fdp2256 + centifr * m_cent * m_ifr2257 + doorbr * m_door * m_br2258 + dooriam * m_door * m_iam2259 + doorfdp * m_door * m_fdp2260 + doorifr * m_door * m_ifr2261 + briam * m_br * m_iam2262 + brfdp * m_br * m_fdp2263 + brifr * m_br * m_ifr2264 + Iamfdp * m_iam * m_fdp2265 + Iamifr * m_iam * m_ifr2266 + fdpifr * m_fdp * m_ifr22672268 ;2269 ;22702271 ;22722273 mtvol= vol+2*vol2*m_vol +volcurb*m_curb+volcbu*m_cbu+volcent*m_cent+voldoor*m_door++volbr*m_br +2273! voliam*m_iam + volfdp*m_fdp +volifr*m_ifr ;2274 elasvol=mtvol*m_vol/phours;2275 mtcurb=volcurb*m_vol+curb;2276 elascurb=mtcurb*m_curb/phours;2277 mcvol=60*mtvol;22782279
NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.NOTE: The data set WORK.ELASQUAD has 1 observations and 83 variables.NOTE: DATA statement used (Total process time): real time 0.06 seconds cpu time 0.06 seconds
2280 proc print data=elasquad;
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Docket No. RM2019-6 Public Representative Comments
2281 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;2282228322842285 run;
NOTE: There were 1 observations read from the data set WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
COLLECTION LOG FULL QUADRATIC SCALED
NOTE: Copyright (c) 2002-2012 by SAS Institute Inc., Cary, NC, USA.NOTE: SAS (r) Proprietary Software 9.4 (TS1M0) Licensed to POSTAL REGULATORY COMMISSION, Site 70021410.NOTE: This session is executing on the X64_7PRO platform.
NOTE: Updated analytical products:
SAS/STAT 12.3 (maintenance) SAS/ETS 12.3 (maintenance)
NOTE: Additional host information:
X64_7PRO WIN 6.1.7601 Service Pack 1 Workstation
NOTE: SAS initialization used: real time 4.04 seconds cpu time 1.39 seconds
1 Libname SUN1 ! 'Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1.1 ! Public Files\Public Folder\Directory 1 Analysis Data Sets';NOTE: Libref SUN was successfully assigned as follows: Engine: V9 Physical Name: Z:\RM2019-6\USPS\Public\USPS-RM2019-6_1\SPR.Prop.1.Fldr.1. Public Files\Public Folder\Directory 1 Analysis Data Sets234 options nodate;5 ods graphics off;67 ****************************************************;8 **** Read in Sunday Data ************************;
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Docket No. RM2019-6 Public Representative Comments
9 ****************************************************;101112 DATA Jun_sunreg1; set sun.June_SUNANA;13
NOTE: There were 2460 observations read from the data set SUN.JUNE_SUNANA.NOTE: The data set WORK.JUN_SUNREG1 has 2460 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.04 seconds
14 DATA Sep_sunreg1; set SUN.sep_SUNANA;15
NOTE: There were 2628 observations read from the data set SUN.SEP_SUNANA.NOTE: The data set WORK.SEP_SUNREG1 has 2628 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.03 seconds
16 DATA Dec_sunreg1; set SUN.dec_SUNANA;17
NOTE: There were 8420 observations read from the data set SUN.DEC_SUNANA.NOTE: The data set WORK.DEC_SUNREG1 has 8420 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.05 seconds cpu time 0.01 seconds
18 data Mar_sunreg1; set sun.mar_sunana;1920 **** Selecting the month to be estimated ***;21
NOTE: There were 3036 observations read from the data set SUN.MAR_SUNANA.NOTE: The data set WORK.MAR_SUNREG1 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.03 seconds
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Docket No. RM2019-6 Public Representative Comments
22 data sunreg2; set mar_sunreg1 ;232425 **** Selecting the Sunday delivery observations ***;26
NOTE: There were 3036 observations read from the data set WORK.MAR_SUNREG1.NOTE: The data set WORK.SUNREG2 has 3036 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
27 data sunreg3; set sunreg2;28 if hours = '.' then delete;29 if ldc = '2400';3031 /*if ldc = '2300' or ldc = '2400';*/32 if vol > 0;333435 *** Converting the characteristic variables to levels35 ! ************;36 *** for calculating ratios for combined LDC23 and 2436 ! observations *****;37
NOTE: Character values have been converted to numeric values at the places given by: (Line):(Column). 28:12NOTE: There were 3036 observations read from the data set WORK.SUNREG2.NOTE: The data set WORK.SUNREG3 has 2684 observations and 21 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
38 data sunreg3; set sunreg3;39 cbuL = cbu * DT ;40 centL = cent * DT ;41 curbL = curb * DT ;42 doorL = door * DT ;43 otherL = other * DT ;4445 FDPl = FDP * DE ;
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Docket No. RM2019-6 Public Representative Comments
46 IAMl = IAM * DE ;47 IFRl = IFR * DE ;48 ODEl = ODE * DE ;49 BRL = BR * DT ;505152 **** Cumulating data by finance number / date ****;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.03 seconds
53 proc sort data=sunreg3; by fin_no date;54
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3 has 2684 observations and 31 variables.NOTE: PROCEDURE SORT used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
55 proc summary data=sunreg3; by fin_no date;56 Var57 cbuL58 centL59 curbL60 doorL61 otherL62 FDPL63 IAML64 IFRL65 ODEL66 BRL67 DT68 DE69 hours70 ndhours71 thours72 vol73 ;74 output out=sunreg3a sum=75 cbuL76 centL
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Docket No. RM2019-6 Public Representative Comments
77 curbL78 doorL79 otherL80 FDPl81 IAMl82 IFRl83 ODEl84 brL85 DT86 DE87 hours88 ndhours89 thours90 vol9192 mean=93 a_cbuL94 a_centL95 a_curbL96 a_doorL97 a_otherL98 a_FDPL99 a_IAML100 a_IFRL101 a_ODEL102 a_BRL103 a_DT104 a_DE105 a_hours106 a_ndhours107 a_thours108 a_vol109 ;110111 ;112113 *** Calculating characteristic variables for combined LDCs113! ***;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3.NOTE: The data set WORK.SUNREG3A has 2684 observations and 36 variables.NOTE: PROCEDURE SUMMARY used (Total process time): real time 0.09 seconds cpu time 0.09 seconds
114 data sunreg3a; set sunreg3a;115 cbu = cbuL / DT ;116 cent = centL / DT ;
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Docket No. RM2019-6 Public Representative Comments
117 curb = curbL / DT ;118 door = doorL / DT ;119 other = otherL / DT ;120121 FDP = FDPL / DE ;122 IAM = IAML / DE ;123 IFR = IFRL / DE ;124 ODE = ODEL / DE ;125 br = BRL / DT ;126
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3A has 2684 observations and 46 variables.NOTE: DATA statement used (Total process time): real time 0.02 seconds cpu time 0.00 seconds
127 DATA sunreg3B; set sunreg3a;128 keep fin_no date129 cbu130 cent131 curb132 door133 other134 FDP135 IAM136 IFR137 ODE138 br139 DT140 DE141 hours142 ndhours143 thours144 vol145 ;146147 run;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3A.NOTE: The data set WORK.SUNREG3B has 2684 observations and 18 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
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148149 *** Constructing variables needed for econometric equation149! ***;150151152153154 data sunreg3C; set sunreg3b;155 vol2=vol*vol;156 curb2 = curb * curb ;157 cbu2 = cbu * cbu ;158 cent2 = cent * cent ;159 door2 = door * door ;160 br2 = br * br ;161 iam2 = iam * iam ;162 fdp2 = fdp * fdp ;163 ifr2 = ifr * ifr ;164165 volcurb=vol*curb;166 volcbu=vol*cbu;167 volcent=vol*cent;168 voldoor=vol*door;169 volbr=vol*br;170 voliam=vol*iam;171 volfdp=vol*fdp;172 volifr=vol*ifr;173 volode=vol*ode;174 curbcbu = curb * cbu ;175 curbcent = curb * cent ;176 curbdoor = curb * door ;177 curbbr = curb * br ;178 curbiam = curb * iam ;179 curbfdp = curb * fdp ;180 curbifr = curb * ifr ;181182 cbucent = cbu * cent ;183 cbudoor = cbu * door ;184 cbubr = cbu * br ;185 cbuiam = cbu * iam ;186 cbufdp = cbu * fdp ;187 cbuifr = cbu * ifr ;188189 centdoor = cent * door ;190 centbr = cent * br ;191 centiam = cent * iam ;192 centfdp = cent * fdp ;193 centifr = cent * ifr ;194195 doorbr = door * br ;196 dooriam = door * iam ;197 doorfdp = door * fdp ;
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198 doorifr = door * ifr ;199200 briam = br * iam ;201 brfdp = br * fdp ;202 brifr = br * ifr ;203204 Iamfdp = Iam * fdp ;205 Iamifr = Iam * ifr ;206207 fdpifr = fdp * ifr ;208209
NOTE: There were 2684 observations read from the data set WORK.SUNREG3B.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.04 seconds
210 proc means; var211212 hours213 ndhours214 thours215 DT216 curb217 cbu218 cent219 door220 other221 br222 DE223 IAM224 FDP225 IFR226 ODE227 vol228 ;229230 output out=regmean mean=231 m_hours232 m_ndhours233 m_thours234 m_DT235 m_curb236 m_cbu237 m_cent238 m_door
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Docket No. RM2019-6 Public Representative Comments
239 m_other240 m_br241 m_DE242 m_IAM243 m_FDP244 m_IFR245 m_ODE246 m_vol;247
NOTE: Writing HTML Body file: sashtml.htmNOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.REGMEAN has 1 observations and 18 variables.NOTE: PROCEDURE MEANS used (Total process time): real time 0.59 seconds cpu time 0.36 seconds
248 data sunreg3c; set sunreg3c;
NOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.SUNREG3C has 2684 observations and 64 variables.NOTE: DATA statement used (Total process time): real time 0.01 seconds cpu time 0.01 seconds
249 proc stdize data=sunreg3c out=sunreg3cnew;250251
NOTE: No VAR statement is given. All numerical variables not named elsewhere make up the first set of variables.WARNING: The scale estimator for variable date is less than or equal to 0. Variable date will not be standardized and a missing value is assigned to its scale estimator.NOTE: There were 2684 observations read from the data set WORK.SUNREG3C.NOTE: The data set WORK.SUNREG3CNEW has 2684 observations and 64 variables.NOTE: PROCEDURE STDIZE used (Total process time): real time 0.03 seconds cpu time 0.01 seconds
252 proc reg data=SUNreg3Cnew outest=quadc;253 model thours=vol vol2 volcurb volcbu volcent voldoor volbr
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253! voliam volfdp volifr254 curb cbu cent door br iam fdp ifr curb2 cbu2254! cent2 door2 br2 iam2 fdp2 ifr2255 curbcbu256 curbcent257 curbdoor258 curbbr259 curbiam260 curbfdp261 curbifr262 cbucent263 cbudoor264 cbubr265 cbuiam266 cbufdp267 cbuifr268 centdoor269 centbr270 centiam271 centfdp272 centifr273 doorbr274 dooriam275 doorfdp276 doorifr277 briam278 brfdp279 brifr280 Iamfdp281 Iamifr282 fdpifr283284285286 /vif white ;287288 f1: test curbcbu,289 curbcent,290 curbdoor,291 curbbr,292 curbiam,293 curbfdp,294 curbifr,295 cbucent,296 cbudoor,297 cbubr,298 cbuiam,299 cbufdp,300 cbuifr,301 centdoor,302 centbr,
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Docket No. RM2019-6 Public Representative Comments
303 centiam,304 centfdp,305 centifr,306 doorbr,307 dooriam,308 doorfdp,309 doorifr,310 briam,311 brfdp,312 brifr,313 Iamfdp,314 Iamifr,315 fdpifr;316317 f3jtvol: test vol, vol2, volcurb, volcbu, volcent,317! voldoor, volbr, voliam, volfdp, volifr; run;
318
NOTE: The data set WORK.QUADC has 1 observations and 60 variables.NOTE: PROCEDURE REG used (Total process time): real time 0.47 seconds cpu time 0.39 seconds
319 data elasquad; merge regmean quadc(drop=_type_);320321 phours = INTERCEPT322 +vol*m_vol +vol2*m_vol*m_vol323 +volcurb*m_vol*m_curb+volcbu*m_vol*m_cbu+volcent*m_v323! ol*m_cent+voldoor*m_vol*m_door+volbr*m_vol*m_br +323! voliam*m_vol*m_iam + volfdp*m_vol*m_fdp +volifr*m_vol*m_ifr324 +curb*m_curb+cbu*m_cbu +cent*m_cent+ door*m_door325 +curb2*m_curb*m_curb+cbu2*m_cbu*m_cbu325! +cent2*m_cent*m_cent+ door2*m_door*m_door326 + br*m_br+ br2*m_br*m_br327 + iam*m_iam + fdp*m_fdp + ifr*m_ifr +327! iam2*m_iam*m_iam + fdp2*m_fdp*m_fdp + ifr2*m_ifr*m_ifr328 + curbcbu * m_curb * m_cbu329 + curbcent * m_curb * m_cent330 + curbdoor * m_curb * m_door331 + curbbr * m_curb * m_br332 + curbiam * m_curb * m_iam333 + curbfdp * m_curb * m_fdp334 + curbifr * m_curb * m_ifr335 + cbucent * m_cbu * m_cent336 + cbudoor * m_cbu * m_door337 + cbubr * m_cbu * m_br338 + cbuiam * m_cbu * m_iam339 + cbufdp * m_cbu * m_fdp
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Docket No. RM2019-6 Public Representative Comments
340 + cbuifr * m_cbu * m_ifr341 + centdoor * m_cent * m_door342 + centbr * m_cent * m_br343 + centiam * m_cent * m_iam344 + centfdp * m_cent * m_fdp345 + centifr * m_cent * m_ifr346 + doorbr * m_door * m_br347 + dooriam * m_door * m_iam348 + doorfdp * m_door * m_fdp349 + doorifr * m_door * m_ifr350 + briam * m_br * m_iam351 + brfdp * m_br * m_fdp352 + brifr * m_br * m_ifr353 + Iamfdp * m_iam * m_fdp354 + Iamifr * m_iam * m_ifr355 + fdpifr * m_fdp * m_ifr356357 ;358 ;359360 ;361362 mtvol= vol+2*vol2*m_vol362! +volcurb*m_curb+volcbu*m_cbu+volcent*m_cent+voldoor*m_door+362! +volbr*m_br + voliam*m_iam + volfdp*m_fdp +volifr*m_ifr ;363 elasvol=mtvol*m_vol/phours;364 mtcurb=volcurb*m_vol+curb;365 elascurb=mtcurb*m_curb/phours;366 mcvol=60*mtvol;367368
NOTE: There were 1 observations read from the data set WORK.REGMEAN.NOTE: There were 1 observations read from the data set WORK.QUADC.NOTE: The data set WORK.ELASQUAD has 1 observations and 83 variables.NOTE: DATA statement used (Total process time): real time 0.03 seconds cpu time 0.04 seconds
369 proc print data=elasquad;370 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;371372373374 run;
NOTE: There were 1 observations read from the data set
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WORK.ELASQUAD.NOTE: PROCEDURE PRINT used (Total process time): real time 0.04 seconds cpu time 0.01 seconds
SUNDAY OUTPUT FULL QUADRATIC WITH ESTIMATED WITH ERRORThe SAS System
The MEANS ProcedureVariable N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
curb
cbu
cent
door
other
br
DE
IAM
FDP
IFR
ODE
vol
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
33.8889909
6.3568816
40.2458725
600.5559199
0.3278959
0.1262009
0.1267505
0.3549808
0.0641718
0.0563836
600.5559199
0.3105419
0.5419724
0.0690464
0.0784393
600.5526412
36.6422673
9.9703193
44.5134535
689.1818796
0.2551530
0.1425660
0.1606803
0.2475542
0.0809493
0.0649420
689.1818796
0.2081660
0.2262575
0.0978172
0.0827383
689.1775982
0.1200000
0
0.1200000
1.0000000
0
0
0
0
-1.11022E-16
0
1.0000000
0
0
0
-8.32667E-17
1.0000000
345.9200000
122.4600000
404.7800000
6533.00
1.0000000
0.9382716
1.0000000
1.0000000
0.8000000
1.0000000
6533.00
1.0000000
1.0000000
1.0000000
1.0000000
6533.00
The SAS System
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The REG ProcedureModel: MODEL1
Dependent Variable: thours Number of Observations Read 2745
Number of Observations Used 2745
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 54 5029421 93137 614.56 <.0001
Error 2690 407671 151.55068
Corrected Total 2744 5437092
Root MSE 12.31059 R-Square 0.9250
Dependent Mean 40.24587 Adj R-Sq 0.9235
Coeff Var 30.58846
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
Intercept 1 -33.25287 27.91176 -1.19 0.2336
29.74434 -1.12 0.2637
0
vol 1 0.05138 0.01061 4.84 <.0001
0.02692 1.91 0.0564
968.39657
vol2 1 -0.00000105
2.643277E-7
-3.99 <.0001
7.228669E-7
-1.46 0.1449
7.31424
volcurb 1 0.05028 0.00829 6.07 <.0001
0.01582 3.18 0.0015
78.09108
volcbu 1 0.04397 0.00972 4.52 <.0001
0.01759 2.50 0.0125
27.38365
volcent 1 0.04948 0.00956 5.17 <.0001
0.01847 2.68 0.0074
95.74602
voldoor 1 0.05595 0.00871 6.43 <.0001
0.01684 3.32 0.0009
161.26836
volbr 1 -0.02858 0.01240 -2.30 0.021 0.03857 -0.74 0.458 6.80609
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Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
3 8
voliam 1 -0.03729 0.00867 -4.30 <.0001
0.02979 -1.25 0.2107
121.41778
volfdp 1 -0.03551 0.00849 -4.18 <.0001
0.02823 -1.26 0.2086
175.19862
volifr 1 -0.03699 0.00993 -3.72 0.0002
0.03234 -1.14 0.2528
29.32668
curb 1 12.52110 37.57902 0.33 0.7390
28.92536 0.43 0.6651
1664.63322
cbu 1 -29.98508 46.97732 -0.64 0.5233
39.49984 -0.76 0.4478
812.14759
cent 1 29.61597 41.02145 0.72 0.4704
36.89724 0.80 0.4222
786.63480
door 1 9.68279 35.55722 0.27 0.7854
37.76603 0.26 0.7977
1402.88578
br 1 -197.51518 66.34511 -2.98 0.0029
70.99925 -2.78 0.0054
336.12059
IAM 1 75.91039 59.76133 1.27 0.2041
70.11623 1.08 0.2791
2802.11752
FDP 1 64.71569 56.32738 1.15 0.2507
74.03992 0.87 0.3822
2940.83979
IFR 1 160.74228 64.98173 2.47 0.0134
86.98541 1.85 0.0647
731.54117
curb2 1 29.75158 27.55627 1.08 0.2804
21.09352 1.41 0.1585
582.70624
cbu2 1 -6.59933 39.57375 -0.17 0.8676
30.45709 -0.22 0.8285
195.99667
cent2 1 11.92150 29.31427 0.41 0.6843
30.78197 0.39 0.6986
174.48754
door2 1 50.38829 24.55560 2.05 0.0403
19.41368 2.60 0.0095
497.49058
br2 1 135.06490 54.75539 2.47 0.0137
58.42210 2.31 0.0209
98.77343
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Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
iam2 1 -12.68261 36.48325 -0.35 0.7281
43.45828 -0.29 0.7704
855.40312
fdp2 1 8.11250 32.89312 0.25 0.8052
46.27006 0.18 0.8608
970.90314
ifr2 1 -90.52639 45.82618 -1.98 0.0483
65.64399 -1.38 0.1680
127.16398
curbcbu 1 14.31982 54.88222 0.26 0.7942
42.84050 0.33 0.7382
105.67202
curbcent 1 79.80855 48.16527 1.66 0.0976
44.54139 1.79 0.0733
38.09208
curbdoor
1 71.59393 45.85004 1.56 0.1185
36.65327 1.95 0.0509
160.75237
curbbr 1 44.67102 63.44578 0.70 0.4814
62.52974 0.71 0.4750
60.38884
curbiam 1 -76.83438 37.11454 -2.07 0.0385
29.54968 -2.60 0.0094
283.94556
curbfdp 1 -87.93389 38.73920 -2.27 0.0233
30.59977 -2.87 0.0041
771.53820
curbifr 1 -120.91496 41.43879 -2.92 0.0036
35.70252 -3.39 0.0007
15.40479
cbucent 1 50.74788 56.72956 0.89 0.3711
49.65566 1.02 0.3069
25.02003
cbudoor 1 31.76371 54.39559 0.58 0.5593
40.46356 0.78 0.4325
59.24088
cbubr 1 63.19146 80.23987 0.79 0.4310
69.90467 0.90 0.3661
16.35473
cbuiam 1 16.06419 46.09645 0.35 0.7275
37.33535 0.43 0.6670
42.30004
cbufdp 1 2.06794 46.82094 0.04 0.9648
38.07954 0.05 0.9567
479.51703
cbuifr 1 -45.43360 51.69329 -0.88 0.3795
45.11982 -1.01 0.3140
9.45057
centdoo 1 107.01557 44.83839 2.39 0.017 44.05082 2.43 0.015 83.90588
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Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
r 1 2
centbr 1 -17.79977 68.37103 -0.26 0.7946
84.57161 -0.21 0.8333
22.56376
centiam 1 -80.75279 38.13536 -2.12 0.0343
36.19413 -2.23 0.0258
155.86560
centfdp 1 -93.35501 38.61589 -2.42 0.0157
36.20054 -2.58 0.0100
106.99510
centifr 1 -115.40789 40.24442 -2.87 0.0042
37.36468 -3.09 0.0020
94.12039
doorbr 1 32.26821 66.19999 0.49 0.6260
65.65090 0.49 0.6231
126.01484
dooriam 1 -92.47181 38.98353 -2.37 0.0178
42.09710 -2.20 0.0281
584.15198
doorfdp 1 -106.73828 40.43924 -2.64 0.0084
43.22508 -2.47 0.0136
762.51477
doorifr 1 -116.37409 40.57520 -2.87 0.0042
47.48967 -2.45 0.0143
33.62971
briam 1 201.29488 62.09882 3.24 0.0012
74.58963 2.70 0.0070
27.27753
brfdp 1 228.83884 63.96195 3.58 0.0004
70.29051 3.26 0.0011
46.95813
brifr 1 97.12951 41.94559 2.32 0.0207
52.15826 1.86 0.0627
10.99859
Iamfdp 1 -2.92510 63.71656 -0.05 0.9634
86.01499 -0.03 0.9729
322.20097
Iamifr 1 -32.78913 75.90970 -0.43 0.6658
100.14286 -0.33 0.7434
68.42406
fdpifr 1 -17.18126 69.88873 -0.25 0.8058
102.58885 -0.17 0.8670
72.48702
The SAS System
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Docket No. RM2019-6 Public Representative Comments
The REG ProcedureModel: MODEL1
Test jtmarallusps Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 425.11184 2.81 <.0001
Denominator 2690 151.55068
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jtmarallusps Results using Heteroscedasticity Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 81.17 <.0001
The SAS System
Obs
m_thours phours elasvol mtvol mcvol mtcurb elascurb
1 40.2459 42.1993 0.90071 0.063291 3.79745 42.7147
0.33190
SUNDAY OUTPUT SCALEDThe SAS System
The MEANS ProcedureVariable N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
2745
2745
2745
2745
33.8889909
6.3568816
40.2458725
600.5559199
36.6422673
9.9703193
44.5134535
689.1818796
0.1200000
0
0.1200000
1.0000000
345.9200000
122.4600000
404.7800000
6533.00
110
Docket No. RM2019-6 Public Representative Comments
Variable N Mean Std Dev Minimum Maximum
curb
cbu
cent
door
other
br
DE
IAM
FDP
IFR
ODE
vol
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
2745
0.3278959
0.1262009
0.1267505
0.3549808
0.0641718
0.0563836
600.5559199
0.3105419
0.5419724
0.0690464
0.0784393
600.5526412
0.2551530
0.1425660
0.1606803
0.2475542
0.0809493
0.0649420
689.1818796
0.2081660
0.2262575
0.0978172
0.0827383
689.1775982
0
0
0
0
-1.11022E-16
0
1.0000000
0
0
0
-8.32667E-17
1.0000000
1.0000000
0.9382716
1.0000000
1.0000000
0.8000000
1.0000000
6533.00
1.0000000
1.0000000
1.0000000
1.0000000
6533.00
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Number of Observations Read 2745
Number of Observations Used 2745
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 54 2538.25581 47.00474 614.56 <.0001
Error 2690 205.74419 0.07648
Corrected Total 2744 2744.00000
Root MSE 0.27656 R-Square 0.9250
111
Docket No. RM2019-6 Public Representative Comments
Dependent Mean 1.95492E-15 Adj R-Sq 0.9235
Coeff Var 1.414679E16
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
Intercept 1 4.2874E-15 0.00528 0.00 1.0000 0.00523 0.00 1.0000 0
vol 1 0.79549 0.16429 4.84 <.0001 0.41674 1.91 0.0564 968.39657
vol2 1 -0.05694 0.01428 -3.99 <.0001 0.03905 -1.46 0.1449 7.31424
volcurb 1 0.28309 0.04665 6.07 <.0001 0.08907 3.18 0.0015 78.09108
volcbu 1 0.12496 0.02763 4.52 <.0001 0.04997 2.50 0.0125 27.38365
volcent 1 0.26725 0.05166 5.17 <.0001 0.09976 2.68 0.0074 95.74602
voldoor 1 0.43083 0.06705 6.43 <.0001 0.12968 3.32 0.0009 161.26836
volbr 1 -0.03174 0.01377 -2.30 0.0213 0.04283 -0.74 0.4588 6.80609
voliam 1 -0.25018 0.05818 -4.30 <.0001 0.19984 -1.25 0.2107 121.41778
volfdp 1 -0.29235 0.06988 -4.18 <.0001 0.23246 -1.26 0.2086 175.19862
volifr 1 -0.10648 0.02859 -3.72 0.0002 0.09310 -1.14 0.2528 29.32668
curb 1 0.07177 0.21540 0.33 0.7390 0.16580 0.43 0.6651 1664.63322
cbu 1 -0.09604 0.15046 -0.64 0.5233 0.12651 -0.76 0.4478 812.14759
cent 1 0.10690 0.14808 0.72 0.4704 0.13319 0.80 0.4222 786.63480
door 1 0.05385 0.19775 0.27 0.7854 0.21003 0.26 0.7977 1402.88578
br 1 -0.28816 0.09679 -2.98 0.0029 0.10358 -2.78 0.0054 336.12059
IAM 1 0.35499 0.27947 1.27 0.2041 0.32790 1.08 0.2791 2802.11751
FDP 1 0.32894 0.28631 1.15 0.2507 0.37634 0.87 0.3822 2940.83979
IFR 1 0.35323 0.14280 2.47 0.0134 0.19115 1.85 0.0647 731.54117
curb2 1 0.13760 0.12744 1.08 0.2804 0.09755 1.41 0.1585 582.70624
cbu2 1 -0.01233 0.07391 -0.17 0.8676 0.05689 -0.22 0.8285 195.99667
cent2 1 0.02836 0.06974 0.41 0.6843 0.07323 0.39 0.6986 174.48754
door2 1 0.24164 0.11776 2.05 0.0403 0.09310 2.60 0.0095 497.49058
br2 1 0.12943 0.05247 2.47 0.0137 0.05598 2.31 0.0209 98.77343
112
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
iam2 1 -0.05368 0.15441 -0.35 0.7281 0.18393 -0.29 0.7704 855.40312
fdp2 1 0.04057 0.16451 0.25 0.8052 0.23141 0.18 0.8608 970.90314
ifr2 1 -0.11761 0.05954 -1.98 0.0483 0.08528 -1.38 0.1680 127.16398
curbcbu 1 0.01416 0.05427 0.26 0.7942 0.04236 0.33 0.7382 105.67202
curbcent 1 0.05399 0.03258 1.66 0.0976 0.03013 1.79 0.0733 38.09208
curbdoor
1 0.10452 0.06694 1.56 0.1185 0.05351 1.95 0.0509 160.75237
curbbr 1 0.02889 0.04103 0.70 0.4814 0.04044 0.71 0.4750 60.38884
curbiam 1 -0.18417 0.08896 -2.07 0.0385 0.07083 -2.60 0.0094 283.94556
curbfdp 1 -0.33287 0.14665 -2.27 0.0233 0.11584 -2.87 0.0041 771.53820
curbifr 1 -0.06046 0.02072 -2.92 0.0036 0.01785 -3.39 0.0007 15.40479
cbucent 1 0.02362 0.02641 0.89 0.3711 0.02312 1.02 0.3069 25.02003
cbudoor 1 0.02373 0.04064 0.58 0.5593 0.03023 0.78 0.4325 59.24088
cbubr 1 0.01681 0.02135 0.79 0.4310 0.01860 0.90 0.3661 16.35473
cbuiam 1 0.01197 0.03434 0.35 0.7275 0.02781 0.43 0.6670 42.30004
cbufdp 1 0.00511 0.11561 0.04 0.9648 0.09403 0.05 0.9567 479.51703
cbuifr 1 -0.01426 0.01623 -0.88 0.3795 0.01417 -1.01 0.3140 9.45057
centdoor 1 0.11542 0.04836 2.39 0.0171 0.04751 2.43 0.0152 83.90588
centbr 1 -0.00653 0.02508 -0.26 0.7946 0.03102 -0.21 0.8333 22.56376
centiam 1 -0.13957 0.06591 -2.12 0.0343 0.06256 -2.23 0.0258 155.86560
centfdp 1 -0.13202 0.05461 -2.42 0.0157 0.05119 -2.58 0.0100 106.99510
centifr 1 -0.14688 0.05122 -2.87 0.0042 0.04755 -3.09 0.0020 94.12039
doorbr 1 0.02889 0.05927 0.49 0.6260 0.05877 0.49 0.6231 126.01484
dooriam 1 -0.30268 0.12760 -2.37 0.0178 0.13779 -2.20 0.0281 584.15198
doorfdp 1 -0.38480 0.14579 -2.64 0.0084 0.15583 -2.47 0.0136 762.51477
doorifr 1 -0.08781 0.03062 -2.87 0.0042 0.03583 -2.45 0.0143 33.62971
briam 1 0.08938 0.02757 3.24 0.0012 0.03312 2.70 0.0070 27.27753
113
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
Standard
Error
t Value
Pr > |t| Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
brfdp 1 0.12944 0.03618 3.58 0.0004 0.03976 3.26 0.0011 46.95813
brifr 1 0.04054 0.01751 2.32 0.0207 0.02177 1.86 0.0627 10.99859
Iamfdp 1 -0.00435 0.09477 -0.05 0.9634 0.12793 -0.03 0.9729 322.20097
Iamifr 1 -0.01886 0.04367 -0.43 0.6658 0.05761 -0.33 0.7434 68.42406
fdpifr 1 -0.01105 0.04495 -0.25 0.8058 0.06598 -0.17 0.8670 72.48702
The SAS System
The REG ProcedureModel: MODEL1
Test jtmarallusps Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 0.21455 2.81 <.0001
Denominator 2690 0.07648
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jtmarallusps Results using Heteroscedasticity Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 81.17 <.0001
The SAS System
The REG ProcedureModel: MODEL1
114
Docket No. RM2019-6 Public Representative Comments
Test jttest Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 198.78604 2599.03 <.0001
Denominator 2690 0.07648
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test jttest Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
10 9364.67 <.0001
The SAS System
Obs m_thours phours elasvol mtvol mcvol mtcurb elascurb
1 40.2459 -20029.04 2.02538 -67.5483
-4052.90 170.081 -.002784395
REGULAR DELIVERY FULL QUADRATIC OUTPUT WITH ERRORThe SAS System
The MEANS ProcedureVariable Label N Mean Std Dev Minimum Maximum
hours
ndhours
thours
hours
15904
15904
15904
15904
45.6714738
7.7350905
53.4065644
723.0963908
46.1237552
16.1475661
58.4637605
744.530819
0.2000000
0
0.2000000
1.0000000
1042.27
515.0700000
1523.39
14366.00
115
Docket No. RM2019-6 Public Representative Comments
Variable Label N Mean Std Dev Minimum Maximum
DT
curb
cbu
cent
door
other
br
IAM
FDP
IFR
ODE
vol
cv
boxes
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
0.3105398
0.1165370
0.0971023
0.3850494
0.0907714
0.0365179
0.2825451
0.5771513
0.0843493
0.0559542
723.0963908
28.8651283
28.8651283
4
0.2895182
0.1620055
0.1380369
0.2883343
0.1024762
0.0703354
0.2090615
0.2440991
0.1308090
0.0821487
744.5308194
54.5701626
54.5701626
0
0
0
0
-1.11022E-16
0
0
0
0
-1.249E-16
1.0000000
0
0
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
14366.00
1851.00
1851.00
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Number of Observations Read 15904
Number of Observations Used 15904
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 65 48712340 749421 2102.89 <.0001
Error 15838 5644294 356.37668
Corrected Total 15903 54356634
116
Docket No. RM2019-6 Public Representative Comments
Root MSE 18.87794 R-Square 0.8962
Dependent Mean 53.40656 Adj R-Sq 0.8957
Coeff Var 35.34761
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
Intercept 1 22.48740 5.98561 3.76 0.0002
11.99503 1.87 0.0608
0
vol 1 0.10851 0.00511 21.23 <.0001
0.01534 7.07 <.0001
646.41800
vol2 1 -0.00000168
9.300616E-8
-18.02 <.0001
3.821817E-7
-4.39 <.0001
11.22393
volcurb 1 -0.03117 0.00365 -8.54 <.0001
0.00742 -4.20 <.0001
50.66667
volcbu 1 -0.03786 0.00401 -9.43 <.0001
0.00797 -4.75 <.0001
21.35130
volcent 1 -0.03082 0.00455 -6.77 <.0001
0.01004 -3.07 0.0021
79.49425
voldoor 1 -0.03579 0.00372 -9.63 <.0001
0.00753 -4.75 <.0001
81.96011
volbr 1 0.03163 0.00601 5.26 <.0001
0.02227 1.42 0.1555
5.33790
voliam 1 -0.01998 0.00434 -4.60 <.0001
0.01636 -1.22 0.2219
81.67365
volfdp 1 -0.02261 0.00440 -5.14 <.0001
0.01629 -1.39 0.1651
129.85183
volifr 1 0.02820 0.00489 5.77 <.0001
0.01907 1.48 0.1393
60.62348
volcv 1 0.00003020 0.00000196 15.44 <.0001
0.00000731 4.13 <.0001
35.80180
cv 1 -0.13357 0.05104 -2.62 0.0089
0.12455 -1.07 0.2836
346.23141
cv2 1 0.00002346 0.00001289 1.82 0.0687
0.00004344 0.54 0.5891
39.78188
117
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
cvcurb 1 0.23605 0.06028 3.92 <.0001
0.09839 2.40 0.0164
15.64929
cvcbu 1 0.26576 0.07314 3.63 0.0003
0.11028 2.41 0.0160
6.13735
cvcent 1 0.02472 0.06330 0.39 0.6961
0.13334 0.19 0.8529
68.31011
cvdoor 1 0.10628 0.05482 1.94 0.0526
0.09168 1.16 0.2464
106.52367
cvbr 1 -0.18640 0.06041 -3.09 0.0020
0.09494 -1.96 0.0496
4.14712
cviam 1 0.14148 0.02842 4.98 <.0001
0.07852 1.80 0.0716
25.01485
cvfdp 1 0.00399 0.03543 0.11 0.9104
0.08165 0.05 0.9611
22.22740
cvifr 1 0.24803 0.05169 4.80 <.0001
0.10090 2.46 0.0140
14.02246
curb 1 -4.73615 17.56658 -0.27 0.7875
22.80422 -0.21 0.8355
1154.23952
cbu 1 -52.66684 20.43952 -2.58 0.0100
27.52846 -1.91 0.0557
489.29493
cent 1 128.45469 17.95447 7.15 <.0001
33.07850 3.88 0.0001
274.09779
door 1 46.81735 13.98621 3.35 0.0008
25.27386 1.85 0.0640
725.70958
br 1 -63.45289 19.55163 -3.25 0.0012
31.16832 -2.04 0.0418
84.38852
IAM 1 -71.81210 15.63975 -4.59 <.0001
26.22188 -2.74 0.0062
477.06495
FDP 1 -79.87058 15.30454 -5.22 <.0001
24.65940 -3.24 0.0012
622.79148
IFR 1 77.47250 16.87983 4.59 <.0001
28.31969 2.74 0.0062
217.56093
curb2 1 26.64062 13.53284 1.97 0.049 11.57835 2.30 0.021 452.08415
118
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
0 4
cbu2 1 25.17117 16.17972 1.56 0.1198
14.83278 1.70 0.0897
132.60529
cent2 1 -163.50455 16.69605 -9.79 <.0001
24.27475 -6.74 <.0001
74.86779
door2 1 7.87223 9.83645 0.80 0.4235
10.01323 0.79 0.4318
296.25933
br2 1 -21.39150 16.37529 -1.31 0.1915
24.03752 -0.89 0.3735
17.08782
iam2 1 56.40686 13.33513 4.23 <.0001
22.30936 2.53 0.0115
234.12864
fdp2 1 38.63119 12.06896 3.20 0.0014
19.93343 1.94 0.0526
405.85785
ifr2 1 -107.32884 14.03475 -7.65 <.0001
27.23779 -3.94 <.0001
64.27727
curbcbu 1 40.41002 23.01920 1.76 0.0792
22.16754 1.82 0.0683
54.75891
curbcent 1 -111.93158 24.47884 -4.57 <.0001
28.34618 -3.95 <.0001
14.50557
curbdoor
1 29.71763 18.69819 1.59 0.1120
17.79732 1.67 0.0950
70.14460
curbbr 1 -20.81919 27.14332 -0.77 0.4431
30.72353 -0.68 0.4980
6.64645
curbiam 1 -26.27624 13.57813 -1.94 0.0530
18.31535 -1.43 0.1514
69.89719
curbfdp 1 1.70969 13.87418 0.12 0.9019
18.69192 0.09 0.9271
395.58513
curbifr 1 -56.82826 17.62256 -3.22 0.0013
22.86781 -2.49 0.0130
8.67881
cbucent 1 -147.70140 27.58135 -5.36 <.0001
31.65366 -4.67 <.0001
8.25733
cbudoor 1 14.85247 22.23239 0.67 0.5041
21.32655 0.70 0.4862
24.41065
119
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
cbubr 1 57.72038 26.62531 2.17 0.0302
29.53539 1.95 0.0507
8.60805
cbuiam 1 35.58205 17.31525 2.05 0.0399
22.30730 1.60 0.1107
17.21459
cbufdp 1 66.51198 16.80549 3.96 <.0001
21.55114 3.09 0.0020
204.74637
cbuifr 1 -51.22058 23.96727 -2.14 0.0326
32.46523 -1.58 0.1147
9.89836
centdoor
1 -123.75314 20.75487 -5.96 <.0001
26.64329 -4.64 <.0001
37.89344
centbr 1 -20.13223 25.93415 -0.78 0.4376
41.00090 -0.49 0.6234
9.64757
centiam 1 8.39680 14.28102 0.59 0.5566
24.36517 0.34 0.7304
33.80365
centfdp 1 53.52780 15.24871 3.51 0.0004
24.32714 2.20 0.0278
29.04229
centifr 1 -38.25973 15.44907 -2.48 0.0133
26.39807 -1.45 0.1473
32.78253
doorbr 1 1.74060 21.49657 0.08 0.9355
29.13522 0.06 0.9524
27.90615
dooriam 1 -61.00513 12.05657 -5.06 <.0001
25.07203 -2.43 0.0150
181.42490
doorfdp 1 -29.97287 12.62502 -2.37 0.0176
25.34282 -1.18 0.2369
223.29474
doorifr 1 -76.87003 14.43040 -5.33 <.0001
27.48568 -2.80 0.0052
26.71116
briam 1 88.31928 14.31945 6.17 <.0001
24.04307 3.67 0.0002
6.14126
brfdp 1 107.42481 22.95348 4.68 <.0001
31.18533 3.44 0.0006
8.91375
brifr 1 83.95444 14.47695 5.80 <.0001
29.59828 2.84 0.0046
12.15006
Iamfdp 1 102.16114 19.02748 5.37 <.000 39.37967 2.59 0.009 77.60910
120
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
1 5
Iamifr 1 19.52663 21.33169 0.92 0.3600
41.10439 0.48 0.6348
19.99275
fdpifr 1 -20.71146 21.03317 -0.98 0.3248
40.17009 -0.52 0.6061
23.49480
The SAS System
The REG ProcedureModel: MODEL1
Test f1 Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 4678.74227 13.13 <.0001
Denominator
15838 356.37668
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f1 Results using Heteroscedasticity
Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 209.42 <.0001
The SAS System
The REG Procedure
121
Docket No. RM2019-6 Public Representative Comments
Model: MODEL1Test f2cv Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 6319.73830 17.73 <.0001
Denominator
15838 356.37668
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f2cv Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
10 103.90 <.0001
The SAS System
Obs m_thours
phours elasvol mtvol mcvol elascv mtcv mccv
1 53.4066 53.6105 0.82183 0.060931 3.65585
0.050402 0.093610 5.61660
REGULAR DELIVERY FULL QUADRATIC SCALED
The SAS System
The MEANS ProcedureVariable Label N Mean Std Dev Minimum Maximum
hours
ndhours
hours
15904
15904
-1.38499E-15
-1.78708E-17
1.0000000
1.0000000
-0.9858580
-0.4790252
21.6070552
31.4186612
122
Docket No. RM2019-6 Public Representative Comments
Variable Label N Mean Std Dev Minimum Maximum
thours
DT
curb
cbu
cent
door
other
br
IAM
FDP
IFR
ODE
vol
cv
boxes
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
-1.75491E-15
6.040328E-16
3.612581E-15
1.697725E-15
1.347458E-15
1.956405E-15
3.592029E-15
5.107473E-15
6.111811E-15
3.892259E-15
4.989525E-15
1.492211E-15
6.040328E-16
-5.05743E-16
-5.05743E-16
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
-0.9100777
-0.9698677
-1.0726092
-0.7193399
-0.7034514
-1.3354269
-0.8857807
-0.5191962
-1.3514929
-2.3644136
-0.6448284
-0.6811333
-0.9698677
-0.5289544
-0.5289544
25.1434978
18.3241623
2.3814056
5.4532894
6.5409868
2.1327691
8.8725835
13.6983981
3.4317891
1.7322824
6.9999064
11.4919114
18.3241623
33.3906807
33.3906807
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Number of Observations Read 15904
Number of Observations Used 15904
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 65 14252 219.25633 2102.89
<.0001
Error 15838 1651.33856 0.10426
123
Docket No. RM2019-6 Public Representative Comments
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Corrected Total 15903 15903
Root MSE 0.32290 R-Square 0.8962
Dependent Mean -1.8909E-15 Adj R-Sq 0.8957
Coeff Var -1.70766E16
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
Intercept 1 -5.8776E-16
0.00256 -0.00 1.0000 0.00256 -0.00 1.0000 0
vol 1 1.38189 0.06510 21.23 <.0001 0.19541 7.07 <.0001 646.41800
vol2 1 -0.15461 0.00858 -18.02 <.0001 0.03525 -4.39 <.0001 11.22393
volcurb 1 -0.15570 0.01823 -8.54 <.0001 0.03704 -4.20 <.0001 50.66667
volcbu 1 -0.11161 0.01183 -9.43 <.0001 0.02351 -4.75 <.0001 21.35130
volcent 1 -0.15467 0.02283 -6.77 <.0001 0.05038 -3.07 0.0021 79.49425
voldoor 1 -0.22313 0.02318 -9.63 <.0001 0.04694 -4.75 <.0001 81.96011
volbr 1 0.03113 0.00592 5.26 <.0001 0.02191 1.42 0.1555 5.33790
voliam 1 -0.10645 0.02314 -4.60 <.0001 0.08715 -1.22 0.2219 81.67365
volfdp 1 -0.15010 0.02918 -5.14 <.0001 0.10813 -1.39 0.1651 129.85183
volifr 1 0.11505 0.01994 5.77 <.0001 0.07782 1.48 0.1393 60.62348
volcv 1 0.23657 0.01532 15.44 <.0001 0.05722 4.13 <.0001 35.80180
cv 1 -0.12467 0.04764 -2.62 0.0089 0.11626 -1.07 0.2836 346.23141
cv2 1 0.02940 0.01615 1.82 0.0687 0.05443 0.54 0.5891 39.78188
cvcurb 1 0.03966 0.01013 3.92 <.0001 0.01653 2.40 0.0164 15.64929
cvcbu 1 0.02305 0.00634 3.63 0.0003 0.00956 2.41 0.0160 6.13735
cvcent 1 0.00827 0.02116 0.39 0.6961 0.04458 0.19 0.8529 68.31011
cvdoor 1 0.05123 0.02643 1.94 0.0526 0.04420 1.16 0.2464 106.52367
124
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
cvbr 1 -0.01609 0.00521 -3.09 0.0020 0.00819 -1.96 0.0496 4.14712
cviam 1 0.06376 0.01281 4.98 <.0001 0.03539 1.80 0.0716 25.01485
cvfdp 1 0.00136 0.01207 0.11 0.9104 0.02782 0.05 0.9611 22.22740
cvifr 1 0.04601 0.00959 4.80 <.0001 0.01872 2.46 0.0140 14.02246
curb 1 -0.02345 0.08699 -0.27 0.7875 0.11293 -0.21 0.8355 1154.23952
cbu 1 -0.14594 0.05664 -2.58 0.0100 0.07628 -1.91 0.0557 489.29493
cent 1 0.30329 0.04239 7.15 <.0001 0.07810 3.88 0.0001 274.09779
door 1 0.23090 0.06898 3.35 0.0008 0.12465 1.85 0.0640 725.70958
br 1 -0.07634 0.02352 -3.25 0.0012 0.03750 -2.04 0.0418 84.38852
IAM 1 -0.25679 0.05593 -4.59 <.0001 0.09377 -2.74 0.0062 477.06495
FDP 1 -0.33348 0.06390 -5.22 <.0001 0.10296 -3.24 0.0012 622.79148
IFR 1 0.17334 0.03777 4.59 <.0001 0.06336 2.74 0.0062 217.56093
curb2 1 0.10717 0.05444 1.97 0.0490 0.04658 2.30 0.0214 452.08415
cbu2 1 0.04587 0.02949 1.56 0.1198 0.02703 1.70 0.0897 132.60529
cent2 1 -0.21697 0.02216 -9.79 <.0001 0.03221 -6.74 <.0001 74.86779
door2 1 0.03527 0.04407 0.80 0.4235 0.04486 0.79 0.4318 296.25933
br2 1 -0.01383 0.01058 -1.31 0.1915 0.01554 -0.89 0.3735 17.08782
iam2 1 0.16573 0.03918 4.23 <.0001 0.06555 2.53 0.0115 234.12864
fdp2 1 0.16511 0.05158 3.20 0.0014 0.08520 1.94 0.0526 405.85785
ifr2 1 -0.15699 0.02053 -7.65 <.0001 0.03984 -3.94 <.0001 64.27727
curbcbu 1 0.03326 0.01895 1.76 0.0792 0.01825 1.82 0.0683 54.75891
curbcent 1 -0.04459 0.00975 -4.57 <.0001 0.01129 -3.95 <.0001 14.50557
curbdoor
1 0.03408 0.02144 1.59 0.1120 0.02041 1.67 0.0950 70.14460
curbbr 1 -0.00506 0.00660 -0.77 0.4431 0.00747 -0.68 0.4980 6.64645
curbiam 1 -0.04143 0.02141 -1.94 0.0530 0.02888 -1.43 0.1514 69.89719
curbfdp 1 0.00628 0.05093 0.12 0.9019 0.06861 0.09 0.9271 395.58513
125
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
curbifr 1 -0.02433 0.00754 -3.22 0.0013 0.00979 -2.49 0.0130 8.67881
cbucent 1 -0.03940 0.00736 -5.36 <.0001 0.00844 -4.67 <.0001 8.25733
cbudoor 1 0.00845 0.01265 0.67 0.5041 0.01214 0.70 0.4862 24.41065
cbubr 1 0.01629 0.00751 2.17 0.0302 0.00833 1.95 0.0507 8.60805
cbuiam 1 0.02183 0.01062 2.05 0.0399 0.01369 1.60 0.1107 17.21459
cbufdp 1 0.14501 0.03664 3.96 <.0001 0.04698 3.09 0.0020 204.74637
cbuifr 1 -0.01722 0.00806 -2.14 0.0326 0.01091 -1.58 0.1147 9.89836
centdoor 1 -0.09398 0.01576 -5.96 <.0001 0.02023 -4.64 <.0001 37.89344
centbr 1 -0.00617 0.00795 -0.78 0.4376 0.01257 -0.49 0.6234 9.64757
centiam 1 0.00875 0.01489 0.59 0.5566 0.02540 0.34 0.7304 33.80365
centfdp 1 0.04844 0.01380 3.51 0.0004 0.02201 2.20 0.0278 29.04229
centifr 1 -0.03631 0.01466 -2.48 0.0133 0.02505 -1.45 0.1473 32.78253
doorbr 1 0.00110 0.01353 0.08 0.9355 0.01833 0.06 0.9524 27.90615
dooriam 1 -0.17451 0.03449 -5.06 <.0001 0.07172 -2.43 0.0150 181.42490
doorfdp 1 -0.09084 0.03826 -2.37 0.0176 0.07681 -1.18 0.2369 223.29474
doorifr 1 -0.07049 0.01323 -5.33 <.0001 0.02521 -2.80 0.0052 26.71116
briam 1 0.03914 0.00635 6.17 <.0001 0.01065 3.67 0.0002 6.14126
brfdp 1 0.03578 0.00764 4.68 <.0001 0.01039 3.44 0.0006 8.91375
brifr 1 0.05176 0.00893 5.80 <.0001 0.01825 2.84 0.0046 12.15006
Iamfdp 1 0.12111 0.02256 5.37 <.0001 0.04668 2.59 0.0095 77.60910
Iamifr 1 0.01048 0.01145 0.92 0.3600 0.02206 0.48 0.6348 19.99275
fdpifr 1 -0.01222 0.01241 -0.98 0.3248 0.02370 -0.52 0.6061 23.49480
The SAS System
The REG ProcedureModel: MODEL1
126
Docket No. RM2019-6 Public Representative Comments
Test f1 Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 1.36885 13.13 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f1 Results using Heteroscedasticity
Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 209.42 <.0001
The SAS System
The REG ProcedureModel: MODEL1
Test f2cv Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 1.84895 17.73 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours
127
Docket No. RM2019-6 Public Representative Comments
Test f2cv Results usingHeteroscedasticity Consistent
Covariance Estimates
DF Chi-Square Pr > ChiSq
10 103.90 <.0001
The SAS System
The REG ProcedureModel: MODEL1
Test f3jtvol Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 11 753.67237 7228.48 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f3jtvol Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
11 24311.4 <.0001
The SAS System
Obs m_thours phours elasvol mtvol mcvol elascv mtcv mccv
1 -1.7549E-15
-1.5546E-15 -0.53694 1.38189 82.9133 -0.040559 -0.12467
-7.48035
128
Docket No. RM2019-6 Public Representative Comments
REGULAR DELIVERY OUTPUT WITH JOINT SIG ERROR
The SAS System
The MEANS ProcedureVariable Label N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
curb
cbu
cent
door
other
br
IAM
FDP
IFR
ODE
vol
cv
boxes
hours
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
45.6714738
7.7350905
53.4065644
723.0963908
0.3105398
0.1165370
0.0971023
0.3850494
0.0907714
0.0365179
0.2825451
0.5771513
0.0843493
0.0559542
723.0963908
28.8651283
28.8651283
46.1237552
16.1475661
58.4637605
744.5308194
0.2895182
0.1620055
0.1380369
0.2883343
0.1024762
0.0703354
0.2090615
0.2440991
0.1308090
0.0821487
744.5308194
54.5701626
54.5701626
0.2000000
0
0.2000000
1.0000000
0
0
0
0
-1.11022E-16
0
0
0
0
-1.249E-16
1.0000000
0
0
1042.27
515.0700000
1523.39
14366.00
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
14366.00
1851.00
1851.00
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours
129
Docket No. RM2019-6 Public Representative Comments
Number of Observations Read 15904
Number of Observations Used 15904
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 65 48712340 749421 2102.89 <.0001
Error 15838 5644294 356.37668
Corrected Total 15903 54356634
Root MSE 18.87794 R-Square 0.8962
Dependent Mean 53.40656 Adj R-Sq 0.8957
Coeff Var 35.34761
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
Intercept 1 22.48740 5.98561 3.76 0.0002
11.99503 1.87 0.0608
0
vol 1 0.10851 0.00511 21.23 <.0001
0.01534 7.07 <.0001
646.41800
vol2 1 -0.00000168
9.300616E-8
-18.02 <.0001
3.821817E-7
-4.39 <.0001
11.22393
volcurb 1 -0.03117 0.00365 -8.54 <.0001
0.00742 -4.20 <.0001
50.66667
volcbu 1 -0.03786 0.00401 -9.43 <.0001
0.00797 -4.75 <.0001
21.35130
volcent 1 -0.03082 0.00455 -6.77 <.0001
0.01004 -3.07 0.0021
79.49425
voldoor 1 -0.03579 0.00372 -9.63 <.0001
0.00753 -4.75 <.0001
81.96011
volbr 1 0.03163 0.00601 5.26 <.0001
0.02227 1.42 0.1555
5.33790
voliam 1 -0.01998 0.00434 -4.60 <.0001
0.01636 -1.22 0.2219
81.67365
130
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
volfdp 1 -0.02261 0.00440 -5.14 <.0001
0.01629 -1.39 0.1651
129.85183
volifr 1 0.02820 0.00489 5.77 <.0001
0.01907 1.48 0.1393
60.62348
volcv 1 0.00003020 0.00000196 15.44 <.0001
0.00000731 4.13 <.0001
35.80180
cv 1 -0.13357 0.05104 -2.62 0.0089
0.12455 -1.07 0.2836
346.23141
cv2 1 0.00002346 0.00001289 1.82 0.0687
0.00004344 0.54 0.5891
39.78188
cvcurb 1 0.23605 0.06028 3.92 <.0001
0.09839 2.40 0.0164
15.64929
cvcbu 1 0.26576 0.07314 3.63 0.0003
0.11028 2.41 0.0160
6.13735
cvcent 1 0.02472 0.06330 0.39 0.6961
0.13334 0.19 0.8529
68.31011
cvdoor 1 0.10628 0.05482 1.94 0.0526
0.09168 1.16 0.2464
106.52367
cvbr 1 -0.18640 0.06041 -3.09 0.0020
0.09494 -1.96 0.0496
4.14712
cviam 1 0.14148 0.02842 4.98 <.0001
0.07852 1.80 0.0716
25.01485
cvfdp 1 0.00399 0.03543 0.11 0.9104
0.08165 0.05 0.9611
22.22740
cvifr 1 0.24803 0.05169 4.80 <.0001
0.10090 2.46 0.0140
14.02246
curb 1 -4.73615 17.56658 -0.27 0.7875
22.80422 -0.21 0.8355
1154.23952
cbu 1 -52.66684 20.43952 -2.58 0.0100
27.52846 -1.91 0.0557
489.29493
cent 1 128.45469 17.95447 7.15 <.0001
33.07850 3.88 0.0001
274.09779
door 1 46.81735 13.98621 3.35 0.000 25.27386 1.85 0.064 725.70958
131
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
8 0
br 1 -63.45289 19.55163 -3.25 0.0012
31.16832 -2.04 0.0418
84.38852
IAM 1 -71.81210 15.63975 -4.59 <.0001
26.22188 -2.74 0.0062
477.06495
FDP 1 -79.87058 15.30454 -5.22 <.0001
24.65940 -3.24 0.0012
622.79148
IFR 1 77.47250 16.87983 4.59 <.0001
28.31969 2.74 0.0062
217.56093
curb2 1 26.64062 13.53284 1.97 0.0490
11.57835 2.30 0.0214
452.08415
cbu2 1 25.17117 16.17972 1.56 0.1198
14.83278 1.70 0.0897
132.60529
cent2 1 -163.50455 16.69605 -9.79 <.0001
24.27475 -6.74 <.0001
74.86779
door2 1 7.87223 9.83645 0.80 0.4235
10.01323 0.79 0.4318
296.25933
br2 1 -21.39150 16.37529 -1.31 0.1915
24.03752 -0.89 0.3735
17.08782
iam2 1 56.40686 13.33513 4.23 <.0001
22.30936 2.53 0.0115
234.12864
fdp2 1 38.63119 12.06896 3.20 0.0014
19.93343 1.94 0.0526
405.85785
ifr2 1 -107.32884 14.03475 -7.65 <.0001
27.23779 -3.94 <.0001
64.27727
curbcbu 1 40.41002 23.01920 1.76 0.0792
22.16754 1.82 0.0683
54.75891
curbcent 1 -111.93158 24.47884 -4.57 <.0001
28.34618 -3.95 <.0001
14.50557
curbdoor
1 29.71763 18.69819 1.59 0.1120
17.79732 1.67 0.0950
70.14460
curbbr 1 -20.81919 27.14332 -0.77 0.4431
30.72353 -0.68 0.4980
6.64645
132
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
curbiam 1 -26.27624 13.57813 -1.94 0.0530
18.31535 -1.43 0.1514
69.89719
curbfdp 1 1.70969 13.87418 0.12 0.9019
18.69192 0.09 0.9271
395.58513
curbifr 1 -56.82826 17.62256 -3.22 0.0013
22.86781 -2.49 0.0130
8.67881
cbucent 1 -147.70140 27.58135 -5.36 <.0001
31.65366 -4.67 <.0001
8.25733
cbudoor 1 14.85247 22.23239 0.67 0.5041
21.32655 0.70 0.4862
24.41065
cbubr 1 57.72038 26.62531 2.17 0.0302
29.53539 1.95 0.0507
8.60805
cbuiam 1 35.58205 17.31525 2.05 0.0399
22.30730 1.60 0.1107
17.21459
cbufdp 1 66.51198 16.80549 3.96 <.0001
21.55114 3.09 0.0020
204.74637
cbuifr 1 -51.22058 23.96727 -2.14 0.0326
32.46523 -1.58 0.1147
9.89836
centdoor
1 -123.75314 20.75487 -5.96 <.0001
26.64329 -4.64 <.0001
37.89344
centbr 1 -20.13223 25.93415 -0.78 0.4376
41.00090 -0.49 0.6234
9.64757
centiam 1 8.39680 14.28102 0.59 0.5566
24.36517 0.34 0.7304
33.80365
centfdp 1 53.52780 15.24871 3.51 0.0004
24.32714 2.20 0.0278
29.04229
centifr 1 -38.25973 15.44907 -2.48 0.0133
26.39807 -1.45 0.1473
32.78253
doorbr 1 1.74060 21.49657 0.08 0.9355
29.13522 0.06 0.9524
27.90615
dooriam 1 -61.00513 12.05657 -5.06 <.0001
25.07203 -2.43 0.0150
181.42490
doorfdp 1 -29.97287 12.62502 -2.37 0.017 25.34282 -1.18 0.236 223.29474
133
Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF
ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value
Pr > |t|
6 9
doorifr 1 -76.87003 14.43040 -5.33 <.0001
27.48568 -2.80 0.0052
26.71116
briam 1 88.31928 14.31945 6.17 <.0001
24.04307 3.67 0.0002
6.14126
brfdp 1 107.42481 22.95348 4.68 <.0001
31.18533 3.44 0.0006
8.91375
brifr 1 83.95444 14.47695 5.80 <.0001
29.59828 2.84 0.0046
12.15006
Iamfdp 1 102.16114 19.02748 5.37 <.0001
39.37967 2.59 0.0095
77.60910
Iamifr 1 19.52663 21.33169 0.92 0.3600
41.10439 0.48 0.6348
19.99275
fdpifr 1 -20.71146 21.03317 -0.98 0.3248
40.17009 -0.52 0.6061
23.49480
The SAS System
The REG ProcedureModel: MODEL1
Test f1 Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 4678.74227 13.13 <.0001
Denominator
15838 356.37668
The SAS System
The REG ProcedureModel: MODEL1
134
Docket No. RM2019-6 Public Representative Comments
Dependent Variable: thours Test f1 Results using Heteroscedasticity
Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 209.42 <.0001
The SAS System
The REG ProcedureModel: MODEL1
Test f2cv Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 6319.73830 17.73 <.0001
Denominator
15838 356.37668
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f2cv Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
10 103.90 <.0001
The SAS System
Obs m_thours
phours elasvol mtvol mcvol elascv mtcv mccv
1 53.4066 53.6105 0.82183 0.060931 3.6558 0.050402 0.093610 5.61660
135
Docket No. RM2019-6 Public Representative Comments
Obs m_thours
phours elasvol mtvol mcvol elascv mtcv mccv
5
REGULAR DELIVERY OUTPUT SCALED
The SAS System
The MEANS ProcedureVariable Label N Mean Std Dev Minimum Maximum
hours
ndhours
thours
DT
curb
cbu
cent
door
other
br
IAM
FDP
IFR
ODE
vol
cv
boxes
hours
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
15904
-1.38499E-15
-1.78708E-17
-1.75491E-15
6.040328E-16
3.612581E-15
1.697725E-15
1.347458E-15
1.956405E-15
3.592029E-15
5.107473E-15
6.111811E-15
3.892259E-15
4.989525E-15
1.492211E-15
6.040328E-16
-5.05743E-16
-5.05743E-16
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
1.0000000
-0.9858580
-0.4790252
-0.9100777
-0.9698677
-1.0726092
-0.7193399
-0.7034514
-1.3354269
-0.8857807
-0.5191962
-1.3514929
-2.3644136
-0.6448284
-0.6811333
-0.9698677
-0.5289544
-0.5289544
21.6070552
31.4186612
25.1434978
18.3241623
2.3814056
5.4532894
6.5409868
2.1327691
8.8725835
13.6983981
3.4317891
1.7322824
6.9999064
11.4919114
18.3241623
33.3906807
33.3906807
The SAS System
The REG Procedure
136
Docket No. RM2019-6 Public Representative Comments
Model: MODEL1Dependent Variable: thours
Number of Observations Read 15904
Number of Observations Used 15904
Analysis of Variance
Source DF Sum ofSquares
MeanSquare
F Value Pr > F
Model 65 14252 219.25633 2102.89
<.0001
Error 15838 1651.33856 0.10426
Corrected Total 15903 15903
Root MSE 0.32290 R-Square 0.8962
Dependent Mean -1.8909E-15 Adj R-Sq 0.8957
Coeff Var -1.70766E16
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
Intercept 1 -5.8776E-16
0.00256 -0.00 1.0000 0.00256 -0.00 1.0000 0
vol 1 1.38189 0.06510 21.23 <.0001 0.19541 7.07 <.0001 646.41800
vol2 1 -0.15461 0.00858 -18.02 <.0001 0.03525 -4.39 <.0001 11.22393
volcurb 1 -0.15570 0.01823 -8.54 <.0001 0.03704 -4.20 <.0001 50.66667
volcbu 1 -0.11161 0.01183 -9.43 <.0001 0.02351 -4.75 <.0001 21.35130
volcent 1 -0.15467 0.02283 -6.77 <.0001 0.05038 -3.07 0.0021 79.49425
voldoor 1 -0.22313 0.02318 -9.63 <.0001 0.04694 -4.75 <.0001 81.96011
volbr 1 0.03113 0.00592 5.26 <.0001 0.02191 1.42 0.1555 5.33790
voliam 1 -0.10645 0.02314 -4.60 <.0001 0.08715 -1.22 0.2219 81.67365
volfdp 1 -0.15010 0.02918 -5.14 <.0001 0.10813 -1.39 0.1651 129.85183
volifr 1 0.11505 0.01994 5.77 <.0001 0.07782 1.48 0.1393 60.62348
volcv 1 0.23657 0.01532 15.44 <.0001 0.05722 4.13 <.0001 35.80180
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Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
cv 1 -0.12467 0.04764 -2.62 0.0089 0.11626 -1.07 0.2836 346.23141
cv2 1 0.02940 0.01615 1.82 0.0687 0.05443 0.54 0.5891 39.78188
cvcurb 1 0.03966 0.01013 3.92 <.0001 0.01653 2.40 0.0164 15.64929
cvcbu 1 0.02305 0.00634 3.63 0.0003 0.00956 2.41 0.0160 6.13735
cvcent 1 0.00827 0.02116 0.39 0.6961 0.04458 0.19 0.8529 68.31011
cvdoor 1 0.05123 0.02643 1.94 0.0526 0.04420 1.16 0.2464 106.52367
cvbr 1 -0.01609 0.00521 -3.09 0.0020 0.00819 -1.96 0.0496 4.14712
cviam 1 0.06376 0.01281 4.98 <.0001 0.03539 1.80 0.0716 25.01485
cvfdp 1 0.00136 0.01207 0.11 0.9104 0.02782 0.05 0.9611 22.22740
cvifr 1 0.04601 0.00959 4.80 <.0001 0.01872 2.46 0.0140 14.02246
curb 1 -0.02345 0.08699 -0.27 0.7875 0.11293 -0.21 0.8355 1154.23952
cbu 1 -0.14594 0.05664 -2.58 0.0100 0.07628 -1.91 0.0557 489.29493
cent 1 0.30329 0.04239 7.15 <.0001 0.07810 3.88 0.0001 274.09779
door 1 0.23090 0.06898 3.35 0.0008 0.12465 1.85 0.0640 725.70958
br 1 -0.07634 0.02352 -3.25 0.0012 0.03750 -2.04 0.0418 84.38852
IAM 1 -0.25679 0.05593 -4.59 <.0001 0.09377 -2.74 0.0062 477.06495
FDP 1 -0.33348 0.06390 -5.22 <.0001 0.10296 -3.24 0.0012 622.79148
IFR 1 0.17334 0.03777 4.59 <.0001 0.06336 2.74 0.0062 217.56093
curb2 1 0.10717 0.05444 1.97 0.0490 0.04658 2.30 0.0214 452.08415
cbu2 1 0.04587 0.02949 1.56 0.1198 0.02703 1.70 0.0897 132.60529
cent2 1 -0.21697 0.02216 -9.79 <.0001 0.03221 -6.74 <.0001 74.86779
door2 1 0.03527 0.04407 0.80 0.4235 0.04486 0.79 0.4318 296.25933
br2 1 -0.01383 0.01058 -1.31 0.1915 0.01554 -0.89 0.3735 17.08782
iam2 1 0.16573 0.03918 4.23 <.0001 0.06555 2.53 0.0115 234.12864
fdp2 1 0.16511 0.05158 3.20 0.0014 0.08520 1.94 0.0526 405.85785
ifr2 1 -0.15699 0.02053 -7.65 <.0001 0.03984 -3.94 <.0001 64.27727
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Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
curbcbu 1 0.03326 0.01895 1.76 0.0792 0.01825 1.82 0.0683 54.75891
curbcent 1 -0.04459 0.00975 -4.57 <.0001 0.01129 -3.95 <.0001 14.50557
curbdoor
1 0.03408 0.02144 1.59 0.1120 0.02041 1.67 0.0950 70.14460
curbbr 1 -0.00506 0.00660 -0.77 0.4431 0.00747 -0.68 0.4980 6.64645
curbiam 1 -0.04143 0.02141 -1.94 0.0530 0.02888 -1.43 0.1514 69.89719
curbfdp 1 0.00628 0.05093 0.12 0.9019 0.06861 0.09 0.9271 395.58513
curbifr 1 -0.02433 0.00754 -3.22 0.0013 0.00979 -2.49 0.0130 8.67881
cbucent 1 -0.03940 0.00736 -5.36 <.0001 0.00844 -4.67 <.0001 8.25733
cbudoor 1 0.00845 0.01265 0.67 0.5041 0.01214 0.70 0.4862 24.41065
cbubr 1 0.01629 0.00751 2.17 0.0302 0.00833 1.95 0.0507 8.60805
cbuiam 1 0.02183 0.01062 2.05 0.0399 0.01369 1.60 0.1107 17.21459
cbufdp 1 0.14501 0.03664 3.96 <.0001 0.04698 3.09 0.0020 204.74637
cbuifr 1 -0.01722 0.00806 -2.14 0.0326 0.01091 -1.58 0.1147 9.89836
centdoor 1 -0.09398 0.01576 -5.96 <.0001 0.02023 -4.64 <.0001 37.89344
centbr 1 -0.00617 0.00795 -0.78 0.4376 0.01257 -0.49 0.6234 9.64757
centiam 1 0.00875 0.01489 0.59 0.5566 0.02540 0.34 0.7304 33.80365
centfdp 1 0.04844 0.01380 3.51 0.0004 0.02201 2.20 0.0278 29.04229
centifr 1 -0.03631 0.01466 -2.48 0.0133 0.02505 -1.45 0.1473 32.78253
doorbr 1 0.00110 0.01353 0.08 0.9355 0.01833 0.06 0.9524 27.90615
dooriam 1 -0.17451 0.03449 -5.06 <.0001 0.07172 -2.43 0.0150 181.42490
doorfdp 1 -0.09084 0.03826 -2.37 0.0176 0.07681 -1.18 0.2369 223.29474
doorifr 1 -0.07049 0.01323 -5.33 <.0001 0.02521 -2.80 0.0052 26.71116
briam 1 0.03914 0.00635 6.17 <.0001 0.01065 3.67 0.0002 6.14126
brfdp 1 0.03578 0.00764 4.68 <.0001 0.01039 3.44 0.0006 8.91375
brifr 1 0.05176 0.00893 5.80 <.0001 0.01825 2.84 0.0046 12.15006
Iamfdp 1 0.12111 0.02256 5.37 <.0001 0.04668 2.59 0.0095 77.60910
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Docket No. RM2019-6 Public Representative Comments
Parameter Estimates
Variable DF ParameterEstimate
StandardError
t Value
Pr > |t|
Heteroscedasticity Consistent
VarianceInflation
StandardError
t Value Pr > |t|
Iamifr 1 0.01048 0.01145 0.92 0.3600 0.02206 0.48 0.6348 19.99275
fdpifr 1 -0.01222 0.01241 -0.98 0.3248 0.02370 -0.52 0.6061 23.49480
The SAS System
The REG ProcedureModel: MODEL1
Test f1 Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 28 1.36885 13.13 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f1 Results using Heteroscedasticity
Consistent Covariance Estimates
DF Chi-Square Pr > ChiSq
28 209.42 <.0001
The SAS System
The REG ProcedureModel: MODEL1
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Docket No. RM2019-6 Public Representative Comments
Test f2cv Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 10 1.84895 17.73 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours Test f2cv Results using
Heteroscedasticity ConsistentCovariance Estimates
DF Chi-Square Pr > ChiSq
10 103.90 <.0001
The SAS System
The REG ProcedureModel: MODEL1
Test f3jtvol Results for Dependent Variable thours
Source DF MeanSquare
F Value Pr > F
Numerator 11 753.67237 7228.48 <.0001
Denominator 15838 0.10426
The SAS System
The REG ProcedureModel: MODEL1
Dependent Variable: thours
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Test f3jtvol Results usingHeteroscedasticity Consistent
Covariance Estimates
DF Chi-Square Pr > ChiSq
11 24311.4 <.0001
The SAS System
Obs m_thours phours elasvol mtvol mcvol elascv mtcv mccv
1 -1.7549E-15
-1.5546E-15 -0.53694 1.38189 82.9133 -0.040559 -0.12467
-7.48035
142