GLM Save

You can save values predicted by the model, residuals, and related measures as new variables in the Data Editor. Many of these variables can be used for examining assumptions about the data. To save the values for use in another IBM® SPSS® Statistics session, you must save the current data file.

Predicted Values. The values that the model predicts for each case.

• Unstandardized. The value the model predicts for the dependent variable.
• Weighted. Weighted unstandardized predicted values. Available only if a WLS variable was previously selected.
• Standard error. An estimate of the standard deviation of the average value of the dependent variable for cases that have the same values of the independent variables.

Diagnostics. Measures to identify cases with unusual combinations of values for the independent variables and cases that may have a large impact on the model.

• Cook's distance. A measure of how much the residuals of all cases would change if a particular case were excluded from the calculation of the regression coefficients. A large Cook's D indicates that excluding a case from computation of the regression statistics changes the coefficients substantially.
• Leverage values. Uncentered leverage values. The relative influence of each observation on the model's fit.

Residuals. An unstandardized residual is the actual value of the dependent variable minus the value predicted by the model. Standardized, Studentized, and deleted residuals are also available. If a WLS variable was chosen, weighted unstandardized residuals are available.

• Unstandardized. The difference between an observed value and the value predicted by the model.
• Weighted. Weighted unstandardized residuals. Available only if a WLS variable was previously selected.
• Standardized. The residual divided by an estimate of its standard deviation. Standardized residuals, which are also known as Pearson residuals, have a mean of 0 and a standard deviation of 1.
• Studentized. The residual divided by an estimate of its standard deviation that varies from case to case, depending on the distance of each case's values on the independent variables from the means of the independent variables.
• Deleted. The residual for a case when that case is excluded from the calculation of the regression coefficients. It is the difference between the value of the dependent variable and the adjusted predicted value.

Coefficient Statistics. Writes a variance-covariance matrix of the parameter estimates in the model to a new dataset in the current session or an external IBM SPSS Statistics data file. Also, for each dependent variable, there will be a row of parameter estimates, a row of standard errors of the parameter estimates, a row of significance values for the t statistics corresponding to the parameter estimates, and a row of residual degrees of freedom. For a multivariate model, there are similar rows for each dependent variable. When Heteroskedasticity-consistent statistics is selected (only available for univariate models), the variance-covariance matrix is calculated using a robust estimator, the row of standard errors displays the robust standard errors, and the significance values reflect the robust errors. You can use this matrix file in other procedures that read matrix files.

Saving New Variables or Parameters for GLM

1. From the menus choose:

   Analyze > General Linear Model

2. Choose Univariate, Multivariate, or Repeated Measures.
3. In the dialog box, click Save.
4. Select the types of variables to be added to the Data Editor.

   or

5. Specify a dataset name or external file to store the covariance matrix.

GLM Multivariate and GLM Repeated Measures are available only if you have SPSS Statistics Standard Edition or the Advanced Statistics Option installed.