Methods for Analyze Binary Response for Response Surface Design

Factor/covariate pattern

Describes a single set of factor/covariate values in a data set. Minitab calculates event probabilities, residuals, and other diagnostic measures for each factor/covariate pattern.

For example, if a data set includes the factors gender and race and the covariate age, the combination of these predictors may contain as many different covariate patterns as subjects. If a data set only includes the factors race and sex, each coded at two levels, there are only four possible factor/covariate patterns. If you enter your data as frequencies, or as successes, trials, or failures, each row contains one factor/covariate pattern.

Design matrix

Minitab generates a design matrix for each design. The first column is a column of one's for the constant term. If the design was blocked into k blocks, there are (k − 1) columns for blocks. Minitab uses the same method of coding blocks as in Factorial Models. This is followed by one column for each main effect. Terms with categorical factors can have more than one column. If the model has squared terms, there is one column for each squared term. The column for a squared term is the product of the corresponding factor with itself. If the model has interaction terms, then there is one column for each interaction term. Interactions that include categorical factors can have more than one column. The column for an interaction term is the product of the two columns that are crossed.

If Minitab removes some terms because the data cannot support them, these terms are not in the stored design matrix. The columns stored match the coefficients that are displayed.

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