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First go to Analyze ? Regression ? Linear and shift api00 into the Dependent field and enroll in the Independent(s) field and click Continue. Then click on Plots. Shift *ZRESID to the Y: field and *ZPRED to the X: field, these are the standardized residuals and standardized predicted values respectively.

The standardized residual is found by dividing the difference of the observed and expected values by the square root of the expected value. The standardized residual can be interpreted as any standard score. The mean of the standardized residual is 0 and the standard deviation is 1.

First go to Analyze ? Regression ? Linear and shift api00 into the Dependent field and enroll in the Independent(s) field and click Continue. Then click on Plots. Shift *ZRESID to the Y: field and *ZPRED to the X: field, these are the standardized residuals and standardized predicted values respectively.

A raw residual is the difference between an observed value and a predicted value in a regression or other relevant statistical tool. A standardized residual is the raw residuals divided by an overall standard deviation of the raw residuals. This provides a consistent measure of the error of your prediction.

To make a scatterplot with these new variables, go to Graphs > Regression Variable Plots. Move the Standardized Residuals under Vertical-Axis Variables and move the Standardized Predicted Values under Horizontal-Axis Variables. Then click on OK. In the Output window, you will find a graph that looks like this.

Create residual plots Select Stat >> Regression >> Regression ... >> Fit Regression Model ... Specify the response and the predictor(s). Under Graphs... Under Residuals for Plots, select either Regular or Standardized. ... Select OK.