Springer Texts in Business and Economics

Dependent Variable: LNC

Подпись: Parameter Standard DF Estimate Error
Подпись: T for HO: Parameter=0 Prob > |T|
Подпись: Variable

Analysis of Variance

Sum of

Mean

Source

DF

Squares

Square

F Value

Prob > F

Model

1

0.04693

0.04693

1.288

0.2625

Error

44

1.60260

0.03642

C Total

45

1.64953

Root MSE

0.19085

R-square

0.0285

Dep Mean

4.84784

Adj R-sq

0.0064

C. V.

3.93675

Parameter Estimates

Подпись: 6.209 0.0001 -1.135 0.2625 INTERCEP 1 5.931889 0.95542530

LNY 1 -0.227003 0.19998321

The income elasticity is —0.227 which is negative! Its standard error is (0.1999) and the t-statistic fortesting this income elasticity is zero is —1.135 which is insignificant with a p-value of 0.26. Hence, we cannot reject the null hypothesis. R2 = 0.0285 and s = 0.19085. This regression is not very useful. The income variable is not significant and the R2 indicates that the

regression explains only 2.8% of the variation in consumption. b. Plot of Residuals, and the 95% confidence interval for the predicted value.

SAS Program for 3.14 Data CIGARETT;

Input OBS STATE $ LNC LNP LNY;

Cards;

Proc reg data=cigarett;
model lnc=lny;

*plot residual. *lny=’*’;

*plot (U95. L95.)*lny=’-’ p.*lny/overlay symbol=‘*’;

output out=out1 r=resid p=pred u95=upper95 195=lower95;

proc plot data=out1 vpercent=75 hpercent=100; plot resid*lny=‘*’;

proc plot data=out1 vpercent=95 hpercent=100; plot (Upper95 Lower95)*lny=‘-’ Pred*lny=‘*’

/overlay;

run;

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Springer Texts in Business and Economics

The General Linear Model: The Basics

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Generalized Least Squares

9.1 GLS Is More Efficient than OLS. a. Equation (7.5) of Chap. 7 gives "ois = " + (X'X)-1X'u so that E("ois) = " as long as X and u …

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