CHAPTER 8
MULTIPLE REGRESSION
- 8.4
- For each of two models give the following points
- (a)
- Centering predictors: (5 points)
- (b)
- Model interpretation: (10 points)
- (c)
- Model assessment: (10 points)
- (d)
- Plots: (10 points)
Also, give (10 points) for model
comparison. Thus the total points on this problem are (80
points).
As an example of how the problem might be solved, two possible models
(using centered predictors CPH and CDAYS) are:
- Model 1 (ADDITIVE) (Plots in
Figures 1 and 2)
- o
- Fitted model:

- o
- R2=0.5409, R2a=0.5235, MSE=25.72.
- o
- The intercept, 15.97, is the
predicted lead level at mean PH and DAYS.
- o
- The change in predicted lead per unit
change in CPH is -5.02, and the change in predicted lead per unit
change in CDAYS is 0.56.
- o
- There is a slight curvature in the
plot of residuals versus fitted values. Plots
of studentized residuals look reasonable.
- Model 2 (INTERACTION) (Plots in
Figures 3 and 4)
- o
- Fitted model:

- o
- R2=0.6568, R2a=0.6370, MSE=19.59.
- o
- The intercept, 15.97, is the
predicted lead level at mean PH and DAYS.
- o
- The change in predicted lead per unit
change in CPH is

and the change in predicted lead per unit change in CDAYS is

- o
- Plots of studentized residuals look
reasonable.
Model 2 is preferred because of
.
- 8.6
- R2=0.6568.
- (10 points) 65.68% of the variation in
LEAD is explained by the model:
R2=SSR/SSTO=1949.8326/2968.7347=0.6568.
- (10 points) The model reduces the
uncertainty in predicting LEAD by 65.68%:
R2=1-SSE/SSTO=1-1018.9021/2968.7347=0.6568.
- 8.8
- The VIFs all equal 1.0, so there is no
multicollinearity at all. The previous conclusions are
unchanged.
Figure 1:
SAS/INSIGHT output, additive model,
lead data.
 |
Figure 2:
Studentized residual plots,
additive model, lead data.
 |
Figure 3:
SAS/INSIGHT output, interaction model,
lead data.
 |
Figure 4:
Studentized residual plots,
interaction model, lead data.
 |
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