User Guide

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Chapter 26
Linear Regre
ssion Plots
Figure 26-4
Linear Regression Plots dialog box
Plots can
aid in the validation of the assumptions of normality, linearity, and equality
of variances. Plots are also useful for detecting outliers, unusual observations, and
influential cases. After saving them as new variables, predicted values, residuals,
and othe
r diagnostics are available in the Data Editor for constructing plots with the
independent variables. The following plots are available:
Scatterplots. You can plot any two of the following: the dependent variable,
standardized predicted values, standardized residuals, deleted residuals, adjusted
predict
ed values, Studentized residuals, or Studentized deleted residuals. Plot the
standardized residuals against the standardized predicted values to check for linearity
and equality of variances.
Source
variable list.
Lists the dependent variable (DEPENDNT) and the following
predicted and residual variables: Standardized predicted values (*ZPRED),
Standardized residuals (*ZRESID), Deleted residuals (*DRESID), Adjusted
predi
cted values (*ADJPRED), Studentized residuals (*SRESID), Studentized
deleted residuals (*SDRESID).
Produce all partial plots. Displays scatterplots of residuals of each independent
varia
ble and the residuals of the dependent variable when both variables are regressed
separately on the rest of the independent variables. At least two independent variables
must be in the equation for a partial plot to be produced.