## Partial Leverage Plots

Another diagnostic tool available in the
fit window is partial leverage plots.
When there is more than one explanatory variable in a model,
the relationship of the residuals to one explanatory variable
can be obscured by the effects of other explanatory variables.
Partial leverage plots attempt to reveal these relationships
(Rawlings 1988, pp. 265 -266).
| Choose **Graphs:Partial Leverage**. |

**Figure 14.8:** Graphs Menu

This displays the graphs shown in Figure 14.9.

**Figure 14.9:** Partial Leverage Plots

In each plot in Figure 14.9, the x-axis
represents the residuals of the explanatory variable
from a model that regresses that explanatory variable
on the remaining explanatory variables.
The y-axis represents the residuals of the response
variable calculated with the explanatory variable omitted.

Two reference lines appear in each plot.
One is the horizontal line Y=0, and the other is
the fitted regression line with slope equal to the
parameter estimate of the corresponding explanatory
variable from the original regression model.
The latter line shows the effect of the
variable when it is added to the model last.
An explanatory variable having little or no effect
results in a line close to the horizontal line Y=0.

Examine the slopes of the lines in the partial
leverage plots. The slopes for the plots
representing **HSS** and **HSE** are nearly 0.
This is not surprising since the coefficients
for the parameter estimates of these two
explanatory variables are nearly 0.
You will examine the effect of removing these
two variables from the model in the section
"Modifying the Model" later in this chapter.

Curvilinear relationships not already included
in the model may also be evident in a partial
leverage plot (Rawlings 1988).
No curvilinearity is evident in any of these plots.

Copyright © 1999 by SAS Institute Inc., Cary, NC, USA. All rights reserved.