• Power Calculations
 • Relative Prediction Variance
 • Power for a Single Parameter
 • Power for a Categorical Effect
The model matrix. See the Fitting Linear Models book for information on the coding for nominal effects. Also, see The Model Matrix in Technical Details.
Note: You can view the model matrix by running Fit Model. Then select Save Columns > Save Coding Table from the red triangle menu for the main report.
The variance of is given by the ith diagonal entry of , where σ2 is the error variance. Denote the ith diagonal entry of by .
The error variance, σ2, is estimated by the MSE, and has degrees of freedom, where n is the number of observations and p is the number of terms other than the intercept in the model.
Under the null hypothesis, the test statistic F0 has an F distribution on 1 and degrees of freedom.
 X Vector of parameters Least squares estimate of β Vector of Anticipated Coefficient values Matrix that defines the test for the categorical effect. The matrix L identifies the values of the parameters in β corresponding to the categorical effect and sets them equal to 0. The null hypothesis for the test of the categorical effect is given by: r Rank of L. Alternatively, r is the number of levels of the categorical effect minus one.
Note: You can view the design matrix by running Fit Model. Then select Save Columns > Save Coding Table from the red triangle menu for the main report.
The error variance, σ2, is estimated by the MSE, and has degrees of freedom, where n is the number of observations and p is the number of terms other than the intercept in the model.
Under the null hypothesis, the test statistic F0 has an F distribution on r and degrees of freedom.
If the true value of β is , then F0 has a noncentral F distribution with noncentrality parameter given by:
 X σ2 Error variance Vector of least squares estimates of the parameters The ith row of X