Constructs a one-way classification model with covariates.

Performs one-way analysis of covariance assuming parallelism and returns an array containing the parallelism tests for the one-way analysis of covariance.

Determines whether the specified Object is equal to the current Object.

Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.

Returns the partial sum of squares for the one-way analysis of covariance.

Returns an array containing the one-way analysis of covariance assuming parallelism.

Returns a matrix of size ngroup by 15 containing the analysis of variance tables for each linear regression model fitted separately to each treatment group.

Returns a matrix of size ncov + 1 by 4 containing statistics for a linear regression model fitted separately for each of the ngroup treatment groups.

Returns an array containing statistics for a linear regression model fitted separately for all ngroup treatments.

Serves as a hash function for a particular type.

Returns a matrix containing the unadjusted means for the covariates and the response variate and the means for the response variate adjusted for the covariates.

Returns a matrix containing statistics for the regression coefficients for the model assuming parallelism.

Returns the R matrix from the QR decomposition.

Gets the Type of the current instance.

Returns a matrix containing the estimated variances and covariances for the adjusted means assuming parallelism.

Returns a matrix containing the estimated variances and covariances for the coefficients returned using GetModelCoefficients.

Creates a shallow copy of the current Object.

Returns a String that represents the current Object.

The number of cases with missing values in covariates or responses. Cases with any missing values are not used in the analysis.