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Fit General Linear ModelStat > ANOVA > General Linear Model > Fit General Linear Model
Use General Linear Model to fit least squares models when you have categorical factors and optional covariates. You can have a balanced or unbalanced design and the model can be hierarchical or non-hierarchical. However, general linear models with random terms must be hierarchical. In a hierarchical model, all lower-order terms that comprise the higher-order terms also appear in the model.
The model can include interaction and polynomial terms, crossed and nested factors, and fixed and random factors.
You can also do the following tasks:
The default model contains the variables that you enter in Factors and Covariates. If you want to add interaction and polynomial terms, use the tools in the Model dialog. To specify random and nested factors, use the tools in the Random / Nest dialog.
Minitab stores the last model that you fit for each response variable. You can use the stored models to quickly generate comparisons of group means, predictions, contour plots, surface plots, overlaid contour plots, factorial plots, and optimized responses.
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If your model includes nested factors or random factors, or if it uses binary coding, there are restrictions on the procedures that you can perform. See Restrictions for General Linear Models for details. |
For more information see Overview of Balanced ANOVA and GLM. See Stored Model Overview for a discussion about how to use stored models. See Design matrix used by General Linear Model to see how Minitab uses a regression approach.
Response: Select the continuous variables that you want to explain or predict with the factors (X). The response is also called the Y variable. If there is more than one response variable, Minitab fits separate models for each response.
Factors: Select the categorical classifications or group assignments, such as a type of raw material, that explain changes in the response. The factor is also called the X variable.
Covariates: Select the continuous variables that explain changes in the response. A covariate is also called the X variable.