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# General Linear Regression Options

Select the output to be generated initially in a regression analysis – the same information can also be displayed after the analysis, using the Further output dialog. You can also request that no constant term is included in the model: this will affect only the parameterization of factor effects, if there are factors in the model; but if not, it will constrain the regression to pass through the origin.

For a general regression model, you can also control the maximum order of interaction to be generated when you use model-formula operators like *. The default is to include all interactions, up to those involving nine variates or factors. (You cannot ask for more than nine.)

## Display

 Model Details of the model that is fitted Summary Summary analysis-of-variance F-probabilities F probabilities for variance ratios Correlations Correlations between the parameter estimates Fitted values Table containing the values of the response variate, the fitted values, standardized residuals and leverages Estimates Estimates of the parameters in the model t-probability t probabilities for the parameter estimates Confidence intervals Confidence intervals for the parameter estimates. The confidence limit can specified as a percentage using the Confidence limit for estimates (%) field. Accumulated Analysis of variance table containing a line for each change in the fitted model Wald tests Wald and F tests for dropping terms from a regression

## Estimate constant term

Specifies whether to include a constant in the regression model. In simple linear regression this omits the intercept, in other words the fitted line is constrained to pass through the origin.

## Weights

A variate of weights can be supplied to give varying influence of each unit on the fit of the model. This would usually correspond to a known pattern of variance of the observations, when the weights would be the reciprocals of the variances.

## Absorbing factor

A factor can be supplied to specify an absorbing factor defining the groups for within-groups linear regression.

## Factorial limit on model terms

For General Linear Regression you can control the maximum order of interaction to be generated when you use model-formula operators like ‘*’. The default is to include all interactions, up to those involving nine variates or factors (you cannot ask for more than nine).