Week 4

Multiple Regression models Estimation Goodness of fit tests

Announcements Midterm on May 5th in Week 6

I sent an email to online students to remind them about registering for the exam!

I reserved computer lab 658 on the 6th floor from 7:30-9:00pm to review SAS procedures for regression analysis.

Outline Last Week:

Straight line regression

This Week: Multiple regression analysis with several predictors Significance test on predictors Goodness of fit test

Next 2 Weeks:

Residual Analysis (model diagnostics) Computing predictions Non linear models Model selection techniques

Airline Index Price vs Jet Fuel Spot prices

10/10/2006

4/28/2007

11/14/2007

6/1/2008

12/18/2008

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Jet Fuel Spot Prices vs Airline Index Weekly data from Jan 2007 to August 2008

Jet Fuel Spot Price

Airline Index Price

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ex Price

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Jet Fuel Spot Prices vs Airline Index Weekly data from Jan 2007 to August 2008

Jet Fuel Spot Price

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ex

Another graph to visualize the relationship

Corr(Airline index, Fuel Price) = -0.972Regression line: Airline Index = 87.55209 - 0.1816 * fuel

More general regression modelConsider one Y variable and k independent variables Xi, e.g.

X1, X2, X3.

Data on n tuples (yi, xi1,xi2,xi3). Scatter plots show linear association between Y and the

X-variables The observations on y can be assumed to satisfy the

following model

y x x x e for i ni i i i i 0 1 1 2 2 3 3 1,...,

Data

Prediction

error

Assumptions on the regression model1. The relationship between the Y-variable and the X-variables is

linear

2. The error terms ei (measured by the residuals)

– have zero mean (E(ei)=0)

– have the same standard deviation e for each fixed x

– are approximately normally distributed - Typically true for large samples!

– are independent (true if sample is S.R.S.)

Such assumptions are necessary to derive the inferential methods for testing and prediction (to be seen later)!

WARNING: if the sample size is small (n<50) and errors are not normal, you can’t use regression methods!

Parameter estimates

Suppose we have a random sample of n observations on Y and on k independent X-variables.

How do we estimate the values of the coefficients ’s in the regression model of Y versus the X-variables?

The parameter estimates are those values for ’s that minimize the sum of the square errors:

Thus the parameter estimates are those values for ’s that will make the model residuals as small as possible!

The fitted model to compute predictions for Y is

kk xxy ˆ...ˆˆˆ 110

Regression Parameter estimates

2110

2 )]...([)ˆ( kkii i

ii xxyyy

Using Linear Algebra for model estimation (section 12.9)

Let be the parameter vector for the regression model in p variables.

The parameter estimates for each beta can be efficiently found using linear algebra as

where X is the (k x k) data matrix for the X-variables and Y is the data vector for the response variable.

Hard to compute by hand – better use a computer!

Tk ),...,,( 10 β

YXX)Xβ T1T (ˆXT is the transpose of matrix XX-1

denotes the inverse of matrix X

EXAMPLE: CPU usage

A study was conducted to examine what factors affect the CPU usage. A set of 38 processes written in a programming language was considered. For each program, data were collected on the

Y = CPU usage (time) in seconds of time, X1= the number of lines (linet) in thousands generated by the process

execution. X2 = number of programs (step) forming the process X3 = number of mounted computer devices (device).

Problem: Estimate the regression model of Y on X1,X2 and X3

DEVICESTEPLINETy 3210ˆˆˆˆˆ

I) Exploratory data step: Are the associations between Y and the x-variables linear?Draw the scatter plot for each pair (Y, Xi)

Lines executed in process Number of programs

Mounted devices

CPU time CPU time

CPU time

Do the plots showlinearity?

PROC REG - SAS OUTPUT

The REG Procedure Parameter Estimates

Parameter StandardVariable Label DF Estimate Error t Value Pr > |t|Intercept --- 1 0.00147 0.01071 0.14 0.8920Linet --- 1 0.02109 0.00271 7.79 <.0001step --- 1 0.00924 0.00210 4.41 <.0001device --- 1 0.01218 0.00288 4.23 0.0002

The fitted regression model is

DEVICESTEPLINETy 012.0009.0021.00014.0ˆ DEVICESTEPLINETy 012.0009.0021.00014.0ˆ

Fitted model

The ’s estimated values measure the changes in Y for changes in X’s.

For instance, for each increase of 1000 lines executed by the process (keeping the other variables fixed), the CPU usage time will increase of 0.021 seconds.

Fixing the other variables, what happens on the CPU time if I add another device?

The fitted regression model is

DEVICESTEPLINETy 012.0009.0021.00014.0ˆ

Interpretation of model parameters

In multiple regression

The coefficient value i of Xi measures the predicted change in Y for any unit increase in Xi while the other independent variables stay constant.

For instance: measures the changes in Y for a unit increase of the variable X2 if the other x-variables X1 and X3 are fixed.

y x x x e for i ni i i i i 0 1 1 2 2 3 3 1,...,

2

SAS procedure for regression analysis: PROC REGPROC REG <DATA=dataset-name>;MODEL yvar=xvar1 xvar2 xvar3 xvar4;PLOT yvar*xvar1; RUN;--------------------------------------------MODEL: defines the variables in the model

PLOT: defines plots of yvar vs x-variables defined in the MODEL statement. It can create multiple plots. Use the syntax:

PLOT yvar *(xvar1 xvar2); ---------------------------------------------

SAS Procedures for correlation valuesThe CORR option in PROC REG computes the correlation matrix for all

the variables listed in MODEL.

PROC REG / CORR;MODEL yvar=xvar1 xvar2 xvar3;RUN;

Alternatively use PROC CORR to compute correlations:

PROC CORR;VAR yvar xvar1 xvar2;

RUN; list of variables. Correlation values are computed for each pair of variables in the list

SAS procedure for scatterplot: PROC GPLOT

PROC GPLOT;PLOT yvar*xvar;RUN;Creates a scatter plot of yvar versus xvar – Both variables

must be defined in the SAS dataset.

PROC GPLOT;PLOT yvar*xvar=zvar; RUN; Creates a scatter plot of yvar versus xvar where points are

labeled according to the z-var classifier.

SAS Code for the CPU usage data

Data cpu;infile “cpudat.txt";input time line step device;linet=line/1000;label time="CPU time in seconds" line="lines in program execution"

step="number of computer programs" device="mounted devices" linet="lines in program (thousand)";

/*Exploratory data analysis *//* computes correlation values between all variables in dataset */proc corr data=cpu;run;/* creates scatterplots between time vs linet, time vs step and

time vs device, respectively */proc gplot data=cpu;plot time*(linet step device);run;

/* Regression analysis: fits model to predict time using linet, step and device*/

proc reg data=cpu;model time=linet step device;plot time*linet /nostat ;run; quit;

If you want to fit a model with no intercept use the following model statement:

model time=linet step device / noint;

Are the estimated values accurate?

Residual Standard Deviation (pg. 632)

Testing effects of individual variables (pg. 652- 655)

How do we measure the accuracy of the estimated parameter values? (section 11.2)

2ˆ)(

11 xxi

e

2

2

ˆ)(

10 xx

x

n ie

For a simple linear regression with one X, the standard deviation of the parameter estimates are defined as:

They are both functions of the error variance regarded as asort of standard deviation (spread) of the points around the line! The error variance is estimated by the residual standard deviation se (a.k.a. root mean square error )

e

2

)ˆ( 2

n

yys ii

eResiduals!

How do we interpret residual standard deviation? Used as a coarse approximation of the prediction error in

predicting Y-values from regression model Probable error in new predictions is

+/- 2 se

se also used in the formula of standard errors of parameter estimates:

They can be computed from the data and measure the noise in the parameter estimates

2

2

ˆ)(

10 xx

x

nss

ie

2ˆ

)(

11 xx

ssi

e

For general regression models with k x-variables

For k predictors (or explanatory variables), the standard errors of the parameter estimates have a complicated form. But they still depend on the error standard deviation

The residual standard deviation or root mean square error is defined as

k+1 = number of parameters ’s

)1(

)ˆ(

)1(

)()

2

kn

yy

kn

ResidualSSlMS(Residuas ii

e

e

This measures the precision of our predictions!

The REG Procedure Analysis of Variance

Sum of MeanSource DF Squares Square F Value Pr > FModel 3 0.59705 0.19902 166.38 <.0001Error 34 0.04067 0.00120Corrected Total 37 0.63772

Root MSE 0.03459 R-Square 0.9362 Dependent Mean 0.15710 Adj R-Sq 0.9306 Coeff Var 22.01536

The root mean square error for the CPU usage regression model is computed above. That gives an estimate of the error standard deviation

se=0.03459

Inference about regression parameters! Regression estimates are affected by random

error The concepts of hypothesis testing and

confidence intervals apply! The t-distribution is used to construct significance

tests and confidence intervals for the “true” parameters of the population.

Tests are used to select those x-variables that have a significant effect on Y

Tests on the slope for straight line regressionConsider the simple straight line case.

We often test the null hypothesis that the slope is equal to zero which is equivalent to “X has no effect on Y”!

Or in statistical terms :

X has a negative effect

Ho: vs Ha: X has a significant effect X has a positive effect

01 01

The test is computed using the t-statistic

2

1

1

1

)(/1

ˆ

)ˆ.(.

0ˆ

xxsest

ie

With t-distribution with n-2 degrees of freedom!

Tests on the parameters in multiple regression

Significance Test on parameter tests hypothesis:

“Xj has no effect on Y” or in statistical termsj

0: vs0:

jj HaHo Xj has a negative effect on Y Xj has a significant effect on Y Xj has a positive effect on Y

The test is given by the t-statistic

with t-distribution with n-(k+1) degrees of freedom

)ˆ.(.

ˆ

j

j

est

Computed by SAS

Assumptions on the data:1. e1, e2, … en are independent of each other.2. The ei are normally distributed with mean zero and

have common variance s.

Tests in SAS

The test p-values for regression coefficients are computed by PROC REG

SAS will produce the two-sided p-value.

If your alternative hypothesis is one-sided (either > or < ), then find the one-sided p-value dividing by 2 the p-value computed by SAS

one-sided p-value = (two-sided p-value)/2

SAS Output

The REG Procedure Parameter Estimates

Parameter StandardVariable Label DF Estimate Error t Value Pr > |t|Intercept --- 1 0.00147 0.01071 0.14 0.8920Linet --- 1 0.02109 0.00271 7.79 <.0001step --- 1 0.00924 0.00210 4.41 <.0001device --- 1 0.01218 0.00288 4.23 0.0002

T-statistic value P-value

T-tests on individual parameter values show that all the x-variables in the model are significant at 5% level (p-values <0.05). We conclude that there is a significant association between Y and each x-variable.

Test on the intercept 0

The REG Procedure Parameter Estimates

Parameter StandardVariable Label DF Estimate Error t Value Pr > |t|Intercept --- 1 0.00147 0.01071 0.14 0.8920Linet --- 1 0.02109 0.00271 7.79 <.0001step --- 1 0.00924 0.00210 4.41 <.0001device --- 1 0.01218 0.00288 4.23 0.0002

The test on the intercept says that the null hypothesis of 0=0 should be accepted. The test p-value is 0.8920.

This means that the model should have no intercept ! This is not recommended though – unless you know that Y=0 when all the x-variables are equal to zero.

What do we do if a model parameter is not significant?

If the t-test on a parameter bj shows that the parameter value is not significantly different from zero, we should:

1. Refit the regression model without the x-variable corresponding to bj.

2. Check if remaining variables are significant

Next

Model diagnostics: Evaluate the goodness of fit for the observed data Analyze if model assumptions are satisfied

Using the model for predictions Computing predictions and evaluating predictions

accuracy

How is a Linear Regression Analysis done? A Protocol

Steps of Regression Analysis

1. Examine the scatterplot of the data.Does the relationship look linear?Are there points in locations they shouldn’t be?Do we need a transformation?

2. Assuming a linear function looks appropriate, estimate the regression parameters.

Least Squares Estimates (done)3. Examine the residuals for systematic inadequacies in the linear model as fit to

the data. (next week)Is there evidence that a more complicated relationship (say a polynomial) should

be examined? (Residual analysis).Are there data points which do not seem to follow the proposed relationship?

(influential values or outliers).4. Test whether there really is a statistically significant linear relationship.

Does model fit the data adequately?Goodness of fit test (F-test for Variances) (next week)

5. Using the model for predictions (next week)

Residual analysis(Sections 11.5 and 13.4)

What are the residuals?

Standard residuals and standardized/studentized residuals Standard residuals measure the difference between the actual y-

values and the predicted values using the regression model:

For analysis we often use the studentized residuals to identify

outliers, i.e. points that do not appear to be consistent with the rest of the data.

A studentized residual is computed as the i-th residual divided by its standard error.

MSE

yy

yyes

yye ii

ii

iii

)ˆ(

)ˆ.(.

)ˆ(

)ˆ( iii yyr

Residual analysis

The studentized residuals follow an approximate t-distribution (or N(0,1) in large datasets).

Detecting outliers:

Observations with | Student ei|>2 (i.e. larger than 2 or smaller than –2) may be outliers.

Use scatter plots of Studentized residuals vs predicted values. Studentized residuals vs x-values

Residual analysis may display problems in the regression analysis and shows if there is some important variation in Y that is not explained by the regression model.

Residual Plots

The analysis of the residuals is useful to detect possible problems and anomalies in the regression

Residual plots are scatter plots of the regression residuals against the X variables or the predicted values.

If model assumptions hold, points should be randomly scattered inside a band centered around the horizontal line at zero (the mean of the residuals).

Checking Assumptions

How well does the model fit?

Do predicted values seem to be placed in the middle of observed values?

Do data satisfy regression assumptions? (Problems seen in plot of X vs. Y will be reflected in residual plot.)

• Constant variance?• Systematic patterns suggest

lack of independence or more complex model?

• Poorly fit observations?

x

y

Res

idua

l

X“Bad cases”

Non linear relationship Non constant variance

“Good case”Points are randomly scattered around the zero line

How do I know I have a problem?

1) Plot predicted values versus residuals.

What is the pattern of the spread in the residuals as the predicted values increase?

• Spread constant.• Spread increases.• Spread decreases then increases.

Acceptable

Problems Problems

Y

YY

x

x x

x x

x

x xx x

x

x

x

x x

x

x

xx

xx

x

xxx

x

x

Residuals are the computed as “observed y – predicted y”iii yyr ˆ

Homoscedasticity (constant variance)

Residual AnalysisResidual analysis may display problems in the regression analysis –

It shows if there is some important variation in Y that is not explained by the regression model.

Plot residuals vs predicted values: To check linearity assumptions & constant variance for the errors;

plot residual.*predicted.; (PROC REG) plot student.*predicted.;

Plot residuals vs each x-variable: To check linearity assumptions for Y and the x-variable;

plot residual.*xvar; (PROC REG) plot student.*xvar;

Draw normal probability plot of residuals: To check normality assumption for the error terms; if points lie close to a line, the errors can be assumed to be approximately normal. Otherwise the assumption of normality is not satisfied.

plot npp.*residual.; (PROC REG)plot npp.*student.;Note the “.”

Plot residuals vs predicted values

To check linearity assumptions & constant variance for the errors:

PLOT student.*predicted.;

Plot residuals vs each x-variable;To check linearity assumptions for Y and the x-

variable;

PLOT student.*linet;

Look for non-random patterns as they may be signs that model assumptions are not satisfied by the data

Residual plots using studentized residuals

Normal probability plot of residualsDraw normal probability plot of residuals;

To check normality assumption for the error terms; if points lie close to a line, the errors can be assumed to be approximately normal. Otherwise the assumption of normality is not satisfied.

SAS: Plot npp.*student.; (PROC REG)

Points look fairly linear.Assumption of normality is satisfied!

SAS Code for the CPU usage data

Data cpu;infile “cpudat.txt";input time line step device;linet=line/1000;label time="CPU time in seconds" line="lines in program execution"

step="number of computer programs" device="mounted devices" linet="lines in program (thousand)";

/*Exploratory data analysis */

proc corr data=cpu;run;

proc gplot data=cpu;plot time*(linet step device);run;

/* Regression analysis*/proc reg data=cpu;model time=linet step device;*/noint;plot (residual. student.)*predicted./nostat; plot student.*(linet step device)/nostat hplots=2

vplots=3;plot npp.*student./nostat ;run; quit;

If you want to fit a model with no intercept use the following model statement:

model time=linet step device / noint;