Regress.MulLinRegress Method

#	Name	Description
1	Y	Defines vector of dependant variable.
2	A	Defines matrix of independant (also X) variables.
3	b	Returns calculated regression coefficiens.
4	Method	Use QR, SVD, or LU solver. Typically QR will yield best compromise between stability and performance.
5	Weights	Defines weights (optional).
6	Constant	If true then intercept term b(0) will be included in calculations. If false, set intercept term b(0) to 0.0.
7	YCalc	Returns vector of calculated dependant variable, where YCalc = A*b.
8	ATA	Returns inverse matrix of normal equations i.e [A(T)*A]^-1.

Result: stored in self (calling object)

Copyusing Dew.Math;
using Dew.Stats.Units;
using Dew.Stats;
namespace Dew.Examples
{
    private void Example()
    {
        Matrix A = new Matrix(0, 0);
        Matrix ATA = new Matrix(0, 0);
        Vector y = new Vector(0);
        Vector b = new Vector(0);
        Vector w = new Vector(0);
        Vector yhat = new Vector(0);
        Vector residuals = new Vector(0);
        Vector BStdDev = new Vector(0);

        TRegStats rs;
        // independent variables
        A.SetIt(4, 2, false, new double[] {1.0, 2.0,
            -3.2, 2.5,
                8.0, -0.5,
                -2.2, 1.8});
        w.SetIt(false, new double[] { 1, 2, 2, 1 }); // weights
        y.SetIt(false, new double[] { -3.0, 0.25, 8.0, 5.5 }); // dependent variables
        Regress.MulLinRegress(y, A, b, w, true, yhat, ATA, TRegSolveMethod.regSolveLQR); //do regression
            // b=(19.093757944, -2.0141843616, -10.082487055)
        Regress.RegressTest(y, yhat, ATA, out rs, residuals, BStdDev, true, w); // do basic regression stats
            // RegStat = (ResidualVar:0.037230395108; R2:0.99965713428;
            // AdjustedR2:0.99897140285; F:1457.7968725; SignifProb: 0.01851663347)
    }
}

Result: stored in self (calling object)