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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).


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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https://www.nalc.org/member-benefits/body/cca_rights_and_benefits.pdf


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


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.

17

https://communities.sas.com/t5/Statistical-Procedures/ERROR-The-TEST-is-not-consistent-or-has-redundant-column/td-p/414798

https://communities.sas.com/t5/Statistical-Procedures/ERROR-The-TEST-is-not-consistent-or-has-redundant-column/td-p/414798


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.

18


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

19


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


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.

21


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

22


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

23


Washington, DC 20268-0001 Phone (202) 789-6862Email: [email protected]

24

mailto:[email protected]


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;

25


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):

26


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

27


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

28


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

29


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 ;

30


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

31


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

32


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

33


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.

34


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

35


234 options nodate;5 ods graphics off;67 ****************************************************;8 **** Read in Sunday Data ************************;9 ****************************************************;101112 DATA Jun_sunreg1; set sun.June_SUNANA;13




16 DATA Dec_sunreg1; set SUN.dec_SUNANA;17



36





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;

37


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 ****;




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


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



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.

40


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 ;

41


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


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

42


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


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


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


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


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


370 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;371372373374 run;


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


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


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


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


Obs


7

SUNDAY OUTPUT SCALED

The SAS System


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


Model: MODEL1Dependent Variable: thours

Number of Observations Read 2745




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


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


Parameter Estimates


Standard

Error

t Value


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

55


Parameter Estimates


Standard

Error

t Value


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




F Value Pr > F

Numerator 28 0.21455 2.81 <.0001


56


The SAS System




28 81.17 <.0001

The SAS System


Test jttest Results for Dependent Variable thours


F Value Pr > F

Numerator 10 198.78604 2599.03 <.0001


The SAS System


Dependent Variable: thours Test jttest Results using

Heteroscedasticity ConsistentCovariance Estimates


10 9364.67 <.0001

The SAS System

57


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







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

58


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

59


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

60


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 ****;


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

61


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

62


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

63


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 ;

64


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):

65



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

66


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

67


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

68


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

69


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;


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

70


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

71


422 data decreg2;423 set decreg1;424425426 **** Selecting the regular delivery observations ***;427 **** Selectin the SPR locations size ******;428 **** Creating a month index *******;429


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 ;

72


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 ****;


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

73


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

74


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.

75


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

76


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

77


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=

78


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

79


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

80



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

81


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;


82


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






83






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


1951 data sunreg3; set sunreg3;1952 cbuL = cbu * DT ;1953 centL = cent * DT ;

84


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 ****;




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

85


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 ***;


2027 data sunreg3a; set sunreg3a;2028 cbu = cbuL / DT ;2029 cent = centL / DT ;2030 curb = curbL / DT ;2031 door = doorL / DT ;

86


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




20612062 *** Constructing variables needed for econometric equation ***;2063206420652066

87


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 ;

88


2118 Iamifr = Iam * ifr ;21192120 fdpifr = fdp * ifr ;21212122


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

89


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,

90


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


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

91


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


2280 proc print data=elasquad;

92


2281 var m_thours phours elasvol mtvol mcvol mtcurb elascurb ;2282228322842285 run;


COLLECTION LOG FULL QUADRATIC SCALED







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 ************************;

93


9 ****************************************************;101112 DATA Jun_sunreg1; set sun.June_SUNANA;13








94




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


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 ;

95


46 IAMl = IAM * DE ;47 IFRl = IFR * DE ;48 ODEl = ODE * DE ;49 BRL = BR * DT ;505152 **** Cumulating data by finance number / date ****;




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

96


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! ***;


114 data sunreg3a; set sunreg3a;115 cbu = cbuL / DT ;116 cent = centL / DT ;

97


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




98


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 ;

99


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


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

100


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

101


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,

102


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


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

103


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


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

104


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


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

105







MeanSquare

F Value Pr > F

Model 54 5029421 93137 614.56 <.0001

Error 2690 407671 151.55068




Coeff Var 30.58846

Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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

106


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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

107


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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

108


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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

109





F Value Pr > F

Numerator 28 425.11184 2.81 <.0001


The SAS System




28 81.17 <.0001

The SAS System

Obs


1 40.2459 42.1993 0.90071 0.063291 3.79745 42.7147

0.33190

SUNDAY OUTPUT SCALEDThe SAS System


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


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






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


111


Dependent Mean 1.95492E-15 Adj R-Sq 0.9235

Coeff Var 1.414679E16

Parameter Estimates


Standard

Error

t Value


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


Parameter Estimates


Standard

Error

t Value


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


Parameter Estimates


Standard

Error

t Value


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




F Value Pr > F

Numerator 28 0.21455 2.81 <.0001


The SAS System




28 81.17 <.0001

The SAS System


114


Test jttest Results for Dependent Variable thours


F Value Pr > F

Numerator 10 198.78604 2599.03 <.0001


The SAS System


Dependent Variable: thours Test jttest Results using



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


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






MeanSquare

F Value Pr > F

Model 65 48712340 749421 2102.89 <.0001

Error 15838 5644294 356.37668


116




Coeff Var 35.34761

Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Test f1 Results for Dependent Variable thours


F Value Pr > F

Numerator 28 4678.74227 13.13 <.0001

Denominator

15838 356.37668

The SAS System


Dependent Variable: thours Test f1 Results using Heteroscedasticity

Consistent Covariance Estimates


28 209.42 <.0001

The SAS System

The REG Procedure

121


Model: MODEL1Test f2cv Results for Dependent Variable thours


F Value Pr > F

Numerator 10 6319.73830 17.73 <.0001

Denominator

15838 356.37668

The SAS System


Dependent Variable: thours Test f2cv Results using



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


hours

ndhours

hours

15904

15904

-1.38499E-15

-1.78708E-17

1.0000000

1.0000000

-0.9858580

-0.4790252

21.6070552

31.4186612

122


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






MeanSquare

F Value Pr > F

Model 65 14252 219.25633 2102.89

<.0001

Error 15838 1651.33856 0.10426

123




MeanSquare

F Value Pr > F



Dependent Mean -1.8909E-15 Adj R-Sq 0.8957

Coeff Var -1.70766E16

Parameter Estimates


StandardError

t Value

Pr > |t|


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


Parameter Estimates


StandardError

t Value

Pr > |t|


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


Parameter Estimates


StandardError

t Value

Pr > |t|


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


126




F Value Pr > F

Numerator 28 1.36885 13.13 <.0001


The SAS System





28 209.42 <.0001

The SAS System


Test f2cv Results for Dependent Variable thours


F Value Pr > F

Numerator 10 1.84895 17.73 <.0001


The SAS System


Dependent Variable: thours

127


Test f2cv Results usingHeteroscedasticity Consistent

Covariance Estimates


10 103.90 <.0001

The SAS System


Test f3jtvol Results for Dependent Variable thours


F Value Pr > F

Numerator 11 753.67237 7228.48 <.0001


The SAS System


Dependent Variable: thours Test f3jtvol Results using



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


REGULAR DELIVERY OUTPUT WITH JOINT SIG ERROR

The SAS System


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



129






MeanSquare

F Value Pr > F

Model 65 48712340 749421 2102.89 <.0001

Error 15838 5644294 356.37668




Coeff Var 35.34761

Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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


Parameter Estimates

Variable DF

ParameterEstimate

StandardError

t Value

Pr > |t|


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




F Value Pr > F

Numerator 28 4678.74227 13.13 <.0001

Denominator

15838 356.37668

The SAS System


134





28 209.42 <.0001

The SAS System




F Value Pr > F

Numerator 10 6319.73830 17.73 <.0001

Denominator

15838 356.37668

The SAS System





10 103.90 <.0001

The SAS System

Obs m_thours


1 53.4066 53.6105 0.82183 0.060931 3.6558 0.050402 0.093610 5.61660

135


Obs m_thours


5

REGULAR DELIVERY OUTPUT SCALED

The SAS System


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


Model: MODEL1Dependent Variable: thours





MeanSquare

F Value Pr > F

Model 65 14252 219.25633 2102.89

<.0001

Error 15838 1651.33856 0.10426



Dependent Mean -1.8909E-15 Adj R-Sq 0.8957

Coeff Var -1.70766E16

Parameter Estimates


StandardError

t Value

Pr > |t|


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

137


Parameter Estimates


StandardError

t Value

Pr > |t|


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

138


Parameter Estimates


StandardError

t Value

Pr > |t|


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

139


Parameter Estimates


StandardError

t Value

Pr > |t|


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




F Value Pr > F

Numerator 28 1.36885 13.13 <.0001


The SAS System





28 209.42 <.0001

The SAS System


140




F Value Pr > F

Numerator 10 1.84895 17.73 <.0001


The SAS System





10 103.90 <.0001

The SAS System


Test f3jtvol Results for Dependent Variable thours


F Value Pr > F

Numerator 11 753.67237 7228.48 <.0001


The SAS System



141


Test f3jtvol Results usingHeteroscedasticity Consistent

Covariance Estimates


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

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