LinearFitBySvd Properties

The LinearFitBySvd type exposes the following members.

Properties

	Name	Description
	AdjustedRSquared	Gives the adjusted coefficient of determination.
	ConditionNumber	Gets the condition number. The decadic logarithm of the condition number is roughly the loss of precision (in digits) during the calculation.
	Covariances	Get the variance-covariance-matrix for the fit.
	EstimatedVariance	Get the estimated residual mean square, also called SigmaSquare..
	NumberOfData	Returns the number of data value.
	NumberOfParameter	Returns the number of parameter (=Order+1) of the fit.
	Parameter	Get the resulting parameters, so that the model y = SUM(parameter[i]*functionbase[i])
	PredictedValues	Gets the predicted dependent values
	RegressionCorrectedSumOfSquares	Gets the regression sum of squares, i.e. SUM(yi`-ymean), where yi` is the predicted ith y value and y mean is the mean value of all y values.
	ResidualSumOfSquares	Gets the sum of ChiSquare for the fit. This is SUM(yi-yi`)^2, where yi is the ith y value and yi` is the ith predicted y.
	ResidualValues	Gets the array of residual values defined as the difference y[i]-ypredicted[i].
	RSquared	Gives the coefficient of determination, also called R^2, squared correlation coefficient. It is a measure, how much of the variability of the y data is accounted for by the regression model.
	Sigma	Get the standard error of regression, defined as Sqrt(SigmaSquare).
	TotalCorrectedSumOfSquares	Gives the corrected sum of squares of y, i.e. SUM(yi-ymean), where yi is the ith y value and ymean is the mean of all y values.