Linear |
The LinearFitBySvd type exposes the following members.
Properties| Name | Description | |
|---|---|---|
 | AdjustedRSquared | Gives the adjusted coefficient of determination. |
| ConditionNumber | Gets the condition number. |
| Covariances | Gets the variance-covariance matrix of the fitted parameters. |
| EstimatedVariance | Gets the estimated residual mean square (also called sigma square). |
| NumberOfData | Gets the number of data points used by the fit. |
| NumberOfParameter | Gets the number of parameters of the fit. |
| Parameter | Gets the fitted parameters such that y = Σ(parameter[i] * functionBase[i]). |
| PredictedValues | Gets the predicted dependent values ŷ[i]. |
| RegressionCorrectedSumOfSquares | Gets the regression corrected sum of squares, i.e. Σ(ŷi - ȳ)^2. |
| ResidualSumOfSquares | Gets the residual sum of squares (RSS) of the fit, i.e. Σ(yi - ŷi)^2. |
| ResidualValues | Gets the residual values defined as y[i] - ŷ[i]. |
| RSquared | Gets the coefficient of determination (R²). |
| Sigma | Gets the standard error of regression. |
| TotalCorrectedSumOfSquares | Gets the total corrected sum of squares of y, i.e. Σ(yi - ȳ)^2. |
See Also