Select the method or formula of your choice.
In This Topic
S
Notation
| Term | Description |
|---|---|
| MSE | mean square error |
R-sq
R2 is also known as the coefficient of determination.
Formula
Notation
| Term | Description |
|---|---|
| yi | i th observed response value |
 | mean response |
 | i th fitted response |
R-sq (adj)
While the calculations for adjusted R2 can produce negative values, Minitab displays zero for these cases.
Notation
| Term | Description |
|---|---|
 | ith observed response value |
 | ith fitted response |
 | mean response |
| n | number of observations |
| p | number of terms in the model |
PRESS
Assesses your model's predictive ability.
Formula
Notation
| Term | Description |
|---|---|
| n | number of observations |
| ei | i th residual |
| hi | i th diagonal element of X(X'X)–1X' |
R-sq (pred)
While the calculations for R2(pred) can produce negative values, Minitab displays zero for these cases.
Notation
| Term | Description |
|---|---|
| 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
| Term | Description |
|---|---|
| n | the number of observations |
| R | the sum of squares for error for the model |
| wi | the weight of the ith observation |
AICc (Akaike's Corrected Information Criterion)
AICc is not calculated when 
.
Notation
| Term | Description |
|---|---|
| n | the number of observations |
| p | the number of coefficients in the model, including the constant |
BIC (Bayesian Information Criterion)
Notation
| Term | Description |
|---|---|
| p | the number of coefficients in the model, including the constant |
| n | the number of observations |
Mallows' Cp
Notation
| Term | Description |
|---|---|
| SSEp | sum of squared errors for the model under consideration |
| MSEm | mean square error for the model with all candidate terms |
| n | number of observations |
| p | number of terms in the model, including the constant |
