How to specify the model terms
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You can fit models with:

·    Up to 9 factors and up to 50 covariates

·    Crossed or nested factors

·    Covariates that are crossed with each other or with factors, or nested within factors

Here are some examples. A is a factor and X is a covariate.

Model Terms

 

A  X  A*X

Fits a full model with a covariate crossed with a factor

A | X

An alternative way to specify the previous model

A  X  X*X

Fits a model with a covariate crossed with itself making a squared term

A  X(A)

Fits a model with a covariate nested within a factor

This model is a generalization of the model used in Minitab's general linear model (GLM) procedure. Any model fit by GLM can also be fit by the life data procedures. For a general discussion of specifying models, see Specifying the model terms and Specifying reduced models. In the regression with life data commands, Minitab assumes any variable in the model is a covariate unless the variable is specified as a factor. In contrast, GLM assumes any variable in the model is a factor unless the variable is specified as a covariate.

Model restrictions

Life data models in Minitab have the same restrictions as general linear models:

·    The model must be full rank, meaning there must be enough data to estimate all the terms in your model. Suppose you have a two-factor crossed model with one empty cell. You can then fit the model with terms A B, but not A B A*B. Do not worry about figuring out whether or not your model is of full rank. Minitab will tell you if it is not. In most cases, eliminating some of the high order interactions in your model (assuming, of course, they are not important) will solve your problem.

·    The model must be hierarchical. In a hierarchical model, if an interaction term is included, all lower order interactions and main effects that comprise the interaction term must appear in the model.