Linear Regression Plots
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 other diagnostic information 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 predicted 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 predicted values (*ADJPRED), Studentized residuals (*SRESID), Studentized deleted residuals (*SDRESID).
Produce all partial plots. Displays scatterplots of residuals of each independent variable 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.
Standardized Residual Plots. You can obtain histograms of standardized residuals and normal probability plots comparing the distribution of standardized residuals to a normal distribution.
If any plots are requested, summary statistics are displayed for standardized predicted values and standardized residuals (*ZPRED and *ZRESID).
Obtaining Plots with a Regression
This feature requires the Statistics Base option.
- From the
- In the Linear Regression dialog box, click Plots.
- For scatterplots, select one variable for the vertical (y) axis and one variable for the horizontal (x) axis. To request additional scatterplots, click Next.