# Regression Estimation Options

The regression method estimates missing values using multiple linear regression. The means, the covariance matrix, and the correlation matrix of the predicted variables are displayed.

**Estimation Adjustment.** The regression method can add a
random component to regression estimates. You can select residuals,
normal variates, Student's *t* variates, or no adjustment.

- Residuals. Error terms are chosen randomly from the observed residuals of complete cases to be added to the regression estimates.
- Normal Variates. Error terms are randomly drawn from a distribution with the expected value 0 and the standard deviation equal to the square root of the mean squared error term of the regression.
- Student's t Variates. Error terms are randomly drawn from a t distribution with the specified degrees of freedom, and scaled by the root mean squared error (RMSE).

**Maximum number of predictors.** Sets a maximum limit on the
number of predictor (independent) variables used in the estimation
process.

**Save completed data.** Writes a dataset in the current session
or an external IBM® SPSS® Statistics data
file, with missing values replaced by values estimated by the regression
method.

To Specify Regression Options

This feature requires the Missing Values option.

- From the
menus choose:
- In the main Missing Value Analysis dialog box, select the variable(s) for which you want to estimate missing values using the regression method.
- Select Regression in the Estimation group.
- To specify predicted and predictor variables, click Variables. See the topic Predicted and Predictor Variables for more information.
- Click Regression.
- Select the regression options you want.