Fit data with success probability responses, assuming increasing integer-independent values:

This is equivalent to:

Fit a model of more than one variable:

Fit data to a linear combination of functions of predictor variables:

Fit a list of rules:

Fit a rule of input values and responses:

Specify a column as the response:

Fit a model with categorical predictor variables:

Obtain a deviance table for the model:

Fit a model given a design matrix and response vector:

See the functional form:

Fit the model referring to the basis functions as x and y:

Obtain a list of available properties for a generalized linear model:

The default gives 95% confidence intervals:

Use 99% intervals instead:

Set the level to 90% within FittedModel:

Fit a generalized linear model:

Compute the covariance matrix using the expected information matrix:

Use the observed information matrix instead:

Fit a binomial model:

Compute the covariance matrix:

Compute the covariance matrix estimating the dispersion by Pearson's :

Fit data to a simple linear regression model:

Fit to a canonical gamma regression model:

Fit to a canonical inverse Gaussian regression model:

Fit a simple linear regression model:

Fit the linear model with intercept zero:

Fit data to a canonical gamma regression model:

Fit data to a gamma regression model with a known Sqrt[x] term:

Fit a Poisson model with canonical Log link:

Use a named link:

Use a pure function for a shifted Sqrt link:

Fit the data treating the first variable as a nominal variable:

Treat both variables as nominal:

Fit a model using equal weights:

Give explicit weights for the data points:

Use WorkingPrecision to get higher precision in parameter estimates:

Obtain the fitted function:

Reduce the precision in property computations after the fitting: