Generalized Linear Models Overviewsee also
Generalized Linear Models OverviewBoth generalized linear models and least squares regression investigate the relationship between a response variable and one or more predictors. A practical difference between them is that generalized linear model techniques are used with categorical response variables, and linear regression techniques are used with continuous response variables.
Minitab provides four generalized linear model techniques that you can use to assess the relationship between one or more predictor variables and a categorical response variable of the following types:
|
Variable |
Number of categories |
Characteristics |
Examples |
|
2 |
two levels |
success, failure | |
|
3 or more |
natural ordering of the levels |
none, mild, severe | |
|
3 or more |
no natural ordering of the levels |
blue, black, red, yellow | |
|
3 or more |
The response variable describes the number of times an event occurs in a finite observation space. |
0, 1, 2, ... |
For a model that has one continuous predictor and a binary response variable, Minitab provides a fifth technique. A Binary Fitted Line Plot quickly describes the relationship between the predictor and the response.
Both generalized linear model techniques and least squares regression techniques estimate parameters in the model so that the fit of the model is optimized. Least squares minimizes the sum of squared errors to obtain maximum likelihood estimates of the parameters, whereas generalized linear models obtain maximum likelihood estimates of the parameters using an iterative-reweighted least squares algorithm [30].