GeneralLinear.MeanSquaresError Method 

Evaluates the mean square due to error (MSE) for the (multi-linear) regression considered.

Return Value

The mean square due to error (MSE).

Remarks

Further Details

Though we assume that the variance of the errors associated to each of the data points used to fit the regression model considered is constant, this constant value is usually not known. The mean square due to error (MSE) is an estimate of the constant variance. The evaluation of the MSE is simply the sum of the squares due to errors (SSE) which can be evaluated using SumSquaresError, divided by the number of degrees of freedom which here is the number of data points (or observations) minus the number of independent variables minus 1.

Preconditions

Please note that before this method is called you must have already performed the following tasks:

1. Set the Fitting Function using SetFunctionBasis
2. Fit the function using SetGeneralFit

See Also

GeneralLinear Class | WebCab.Libraries.Statistics.CurveFitting Namespace