Adds an intercept parameter to the model and recalculates the model parameters.

Adds the given observation to the model, and recalculates the model parameters.

Adds the given observations to the model, and recalculates the model parameters.

Adds a predictor to the model, and recalculates the model parameters.

Adds predictors to the model, and recalculates the model parameters.

Creates a deep copy of this LinearRegression.

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

Convienence method for determining if the first column of a matrix is all ones.

Serves as a hash function for a particular type.

Gets the Type of the current instance.

Returns the value of the dependent variable predicted by the model for the given set of predictor values.

Returns the values of the dependent variable predicted by the model for the given sets of predictor values.

Returns a confidence interval for the value of the dependent variable predicted by the model for the given set of predictor values.

Recalculates the model parameters.

Removes the intercept parameter from the model, and recalculates the model parameters.

Removes the row at the indicated index from the predictor matrix and the corresponding element from the observation vector, and recalculates the model parameters.

Removes the specified rows from the predictor matrix, and recalculates the model parameters.

Removes the specified predictor from the model, and recalculates the model parameters.

Removes the specified predictors from the model, and recalculates the model parameters.

Returns a String that represents the current Object.

An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity.

An index which measures how much the variance of a coefficient (square of the standard deviation) is increased because of collinearity.