Model Fitting: Linear Regression |
The Output Variables Tab
You can use the Output Variables tab (Figure 21.17) to add analysis variables to the data table. If you request a plot that uses one of the output variables, then that variable is automatically created even if you did not explicitly select the variable on the Output Variables tab.
The following list describes each output variable and indicates how the output variable is named.
represents the name of the response variable.
- Predicted values
- adds predicted values. The variable is named RegP_
.
- Confidence limits for means
- adds 95% confidence limits for the expected value (mean). The variables are named RegLclm_
and RegUclm_
.
- Prediction limits for individuals
- adds 95% confidence limits for an individual prediction. The variables are named RegLcli_
and RegUcli_
.
- Raw residuals
- adds residuals, calculated as observed minus predicted values. The variable is named RegR_
.
- Internally studentized residuals
- adds internally studentized residuals, which are the residuals divided by their standard errors. (These correspond to the STUDENT= option in the OUTPUT statement.) The variable is named RegIntR_
.
- Externally studentized residuals
- adds externally studentized residuals, which are studentized residuals with the current observation deleted. (These correspond to the RSTUDENT= option in the OUTPUT statement.) The variable is named RegExtR_
.
- Cook's D
- adds Cook's
influence statistic. The variable is named RegCooksD_
.
- Leverage (H)
- adds the leverage statistic. The variable is named RegH_
.
- PRESS residuals
- adds the PRESS residuals. This is the
th residual divided by
, where
is the leverage, and where the model has been refit without the
th observation. The variable is named RegPRESS_
.
- COVRATIO (influence on covariance of coefficients)
- adds the covariance ratio. This is the
th residual divided by
, where
is the leverage, and where the model has been refit without the
th observation. The variable is named RegCovRatio_
.
- DFFITS (influence on predicted values)
- adds the standard influence of observation on the predicted value. The variable is named RegDFFITS_
.
- DFBETAS (influence on coefficients)
- adds
variables, where
is the number of parameters in the model. The variables are scaled measures of the change in each parameter estimate and are calculated by deleting the
th observation. Large values of DFBETAS indicate observations that are influential in estimating a given parameter. Belsley, Kuh, and Welsch (1980) recommend
as a size-adjusted cutoff. The variables are named DFB_
, where
is the name of the
th regressor (including the intercept).
Figure 21.17: The Output Variables Tab
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