Regress.RegressTest Method

#	Name	Description
1	Y	Dependant variables.
2	YCalc	Estimated (calculated) dependant variables.
3	ATA	Inverse matrix of normal equations i.e [A(T)*A]^-1.
4	Weights	Model weights (optional).
5	Constant	If true then include intercept term b(0) in calculations. If false, set intercept term b(0) to 0.0.
6	RegStat	Returns regression statistics parameters.
7	Residuals	Returns residual errors.
8	BStdDev	Returns standard deviation.

Result: stored in self (calling object)

#	Name	Description
1	Y	Dependant variables.
2	YCalc	Estimated (calculated) dependant variables.
3	Weights	Model weights (optional).
4	NumPars	Number of variables (parameters) in ML model A*b=y (number of columns in A matrix or number of rows in b).
5	Constant	If true then include intercept term b(0) in calculations. If false, set intercept term b(0) to 0.0.
6	RegStat	Returns regression statistics parameters.

Result: stored in self (calling object)

CopyUses Regress, MtxExpr;
procedure Example;
var y,b,w,yhat, resid, bstd: Vector;
A, ATA : Matrix;
RegStat : TRegStats;
begin
    A.SetIt(4,2,false,[1.0, 2.0,
    -3.2, 2.5,
    8.0,    -0.5,
    -2.2, 1.8]); // independent variables
    w.SetIt(false,[1,2,2,1]); // weights
    y.SetIt(false,[-3.0, 0.25, 8.0, 5.5]); // dependent variables
    MulLinRegress(y,A,b,w,true,yhat,ATA); //do regression
    // b=(19.093757944, -2.0141843616, -10.082487055)
    RegressTest(y,yhat,ATA,RegStat,resid, bstd, true,w); // do basic regression stats
    // RegStat = (ResidualVar:0.037230395108; R2:0.99965713428;
    // AdjustedR2:0.99897140285; F:1457.7968725; SignifProb: 0.01851663347)
end;