Term | Description |
---|---|
MSE | mean square error |
R^{2} is also known as the coefficient of determination.
Term | Description |
---|---|
y_{i} | i ^{th} observed response value |
mean response | |
i ^{th} fitted response |
While the calculations for adjusted R^{2} can produce negative values, Minitab displays zero for these cases.
Term | Description |
---|---|
i^{th} observed response value | |
i^{th} fitted response | |
mean response | |
n | number of observations |
p | number of terms in the model |
While the calculations for R^{2}(pred) can produce negative values, Minitab displays zero for these cases.
Term | Description |
---|---|
y_{i} | i ^{th} observed response value |
mean response | |
n | number of observations |
e_{i} | i ^{th} residual |
h_{i} | i ^{th} diagonal element of X(X'X)^{–1}X' |
X | design matrix |
Term | Description |
---|---|
n | number of observations |
e_{i} | i^{th} residual |
h_{i} | i^{th} diagonal element of X (X' X)^{-1}X' |
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 |
AICc is not calculated when .
Term | Description |
---|---|
n | the number of observations |
p | the number of coefficients in the model, including the constant |
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 |