Methods and formulas for goodness-of-fit statistics in Fit General Linear Model

Select the method or formula of your choice.

S

Term	Description
MSE	mean square error

R² is also known as the coefficient of determination.

While the calculations for adjusted R² can produce negative values, Minitab displays zero for these cases.

While the calculations for R²(pred) can produce negative values, Minitab displays zero for these cases.

For unweighted analyses, Minitab uses the following equation:

For an analysis that has weights for the observations, Minitab uses the following equation:

Observations with weights of 0 are not in the analysis.

Term	Description
n	the number of observations
R	the sum of squares for error for the model
w_i	the weight of the i^th observation

Minitab does not calculate AICc for Fit General Linear Model when the model includes random factors.

AICc is not calculated when .

Term	Description
n	the number of observations
p	the number of coefficients in the model, including the constant

Minitab does not calculate BIC for Fit General Linear Model when the model includes random factors.

Term	Description
p	the number of coefficients in the model, including the constant
n	the number of observations

Term	Description
SSE_p	sum of squared errors for the model under consideration
MSE_m	mean square error for the model with all candidate terms
n	number of observations
p	number of terms in the model, including the constant