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

Select the method or formula of your choice.

## S

### Notation

TermDescription
MSEmean square error

## R-sq

R2 is also known as the coefficient of determination.

### Notation

TermDescription
yi i th observed response value
mean response
i th fitted response

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

### Notation

TermDescription
ith observed response value
ith fitted response
mean response
nnumber of observations
pnumber of terms in the model

## R-sq (pred)

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

### Notation

TermDescription
yi i th observed response value
mean response
n number of observations
ei i th residual
hi i th diagonal element of X(X'X)–1X'
X design matrix

## Log-likelihood

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.

### Notation

TermDescription
nthe number of observations
Rthe sum of squares for error for the model
withe weight of the ith observation

## AICc (Akaike's Corrected Information Criterion)

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

AICc is not calculated when .

### Notation

TermDescription
nthe number of observations
pthe number of coefficients in the model, including the constant

## BIC (Bayesian Information Criterion)

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

### Notation

TermDescription
pthe number of coefficients in the model, including the constant
nthe number of observations

## Mallows' Cp

### Notation

TermDescription
SSEpsum of squared errors for the model under consideration
MSEmmean square error for the model with all candidate terms
nnumber of observations
pnumber of terms in the model, including the constant