Use Fit General Linear Model to fit least squares models when you have a continuous response, categorical factors, and optional covariates. You can include interaction and polynomial terms, crossed and nested factors, and fixed and random factors.
For example, an engineer at a glass manufacturer wants to test the effect of glass type on the light output of an oscilloscope. Temperature, a covariate, may also affect the light output. The engineer uses a general linear model to determine whether three types of glass affect the light output while accounting for changes in temperature.
After you perform the analysis, Minitab stores the model so that you can do any of the following:
- Compare group means.
- Predict the response for new observations.
- Plot the relationships among the variables.
- Find values that optimize multiple responses.
For more information, go to the Stored model overview
Where to find this analysis
To fit a general linear model, choose .
When to use an alternate analysis
- For a model with random factors, you usually use Fit Mixed Effects Model so that you can use the Restricted Maximum Likelihood estimation method (REML).
- If you have primarily continuous predictor variables, you can get similar model results with Fit Regression Model.
- If you have one or two categorical factors and want to compare the level means to the overall mean for data that follow the normal, binomial, or Poisson distributions, use Analysis of Means.
- If you have only categorical variables for both the response and factors, go to What is a generalized linear model? to learn which type of regression analysis to use.
- If you want to test the equality of the standard deviations between groups, use Test for Equal Variances.
- If you have multiple response variables that are correlated and a common set of factors, use General MANOVA, which has more power and can detect multivariate response patterns.