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Generalized Linear Model Further Output

Generates further output after fitting a generalized linear model.


Model Details of the model that is fitted
Summary Summary analysis-of-deviance
F-probabilities Approximate F probabilities for deviance 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-probabilities Approximate 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 deviance table containing a line for each change in the fitted model
Wald tests Wald and F tests for dropping terms from a regression (not available for multinomial or ordinal regression)

Model checking

You can generate diagnostic plots for Model Checking.

Fitted model

You can draw a Graph of fitted model which shows the relationship of the response variate with one explanatory variate or one grouping factor or both. The fitted relationship is shown as one or more lines and the partial residuals are positioned with respect to the lines to represent the observed data.

Dispersion parameter

Controls whether the dispersion parameter for the variance of the response is estimated from the residual mean square of the fitted model, or fixed at a given value. The dispersion parameter (fixed or estimated) is used when calculating standard errors and standardized residuals. In models with the binomial, Poisson, negative binomial, geometric and exponential distributions, the dispersion should be fixed at 1 unless a heterogeneity parameter is to be estimated. Fixing the Dispersion parameter on the Generalized Linear Models Further Output dialog only affects output generated here. It does not alter the Dispersion setting on the Generalized Linear Models Options diaog, or the status of the fitted model.

Power calculations

You can calculate the power for a regression model.

Permutation test

Lets you perform random permutation tests for a regression model. This can be used to generate probabilities for deviances or deviance ratios in generalized linear models, instead of using the customary chi-square or F distributions.

Updated on June 13, 2019

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