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).