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).