Fit General Linear Model - Coding
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Stat > ANOVA > General Linear Model > Fit General Linear Model > Coding

Use to:

·    Specify the coding scheme for factors

·    Specify the reference level for each factor

·    Standardize the covariates

Standardizing the continuous predictors can improve the interpretation of the model for specific conditions. You can standardize the continuous predictors using the following methods:

·    Center the covariates by subtracting the mean: This method helps reduce multicollinearity, which improves the precision of the coefficient estimates. This method is helpful when your model contains highly correlated covariates, higher-order terms, and interaction terms. Each coefficient represents the expected change in the response given a one unit change in the covariate, using the original measurement scale.

·    Adjust the scale of the covariates by dividing by the standard deviation: This method allows you to compare the size of the coefficients because they use a comparable scale. This approach is helpful when you want to know which covariates have a larger effect, while controlling for differences in scale. However, each coefficient represents the expected change in the response given a change of one standard deviation in the covariate.

Dialog box items

Coding for Factors: Use when you have categorical factors. To perform the analysis, Minitab needs to recode the categorical data. Base your decision on whether you want to compare the levels to the overall mean or the mean of a reference level.

(- 1, 0, +1): Choose to estimate the difference between each level mean and the overall mean.

(1, 0): Choose to estimate the difference between each level mean and the reference level's mean.

Factor reference level: If you choose the (1, 0) coding scheme, the reference level  table becomes active in the dialog box. Minitab compares the means of the nonreference level(s) to the reference level. Changing the reference level does not affect the overall significance, but it can make the results more meaningful.

Categorical predictor: Shows all the names of categorical predictors in your model. This column does not take any input.

Reference level:  Choose a reference level for each factor.

Standardize covariates: Use to control whether Minitab standardizes the covariates. The standardized covariates are only used to fit the model and are not stored in the worksheet.

Do not standardize: Choose to use your original data for the covariates.

Subtract the mean, then divide by the standard deviation: Choose to both center the covariates and to place them on a comparable scale.

Subtract the mean: Choose to center the covariates.

Divide by the standard deviation: Choose to use a comparable scale for all covariates.

Subtract a specified value, then divide by another: Choose to specify values rather than the mean and standard deviation estimates from the sample.

Covariate: Shows all of the names of covariates in your model. This column does not take any input.

Subtract: Type the value to subtract from each covariate.

Divide by: Type the value that Minitab uses to divide the result of the subtraction.

Specify low and high levels to code as -1 and +1: Choose to transform the data linearly. All data values that fall between the Low and High values that you specify are coded to fall between -1 and +1. Designed experiments (DOE) use this scheme.

Covariate: Shows all of the names of covariates in your model. This column does not take any input.

Low: Type a value to code as -1. The default is the minimum value in the sample.

High: Type a value to code as +1. The default is the maximum value in the sample.