private double GetValue()
{
//var yDispersion = StatCharacteristicModel.StandartDeviationSkew.GetValue(lr.yData);
//yDispersion *= yDispersion;
//return 1 - (lr.N - lr.Scores.Count) * lr.ResiduesDisp / (lr.N * yDispersion);
double top = 0;
double bottom = 0;
for (int i = 0; i < lr.N; i++)
{
top += (lr.yData[i] - lr.Calculate(lr.xData[i])) * (lr.yData[i] - lr.Calculate(lr.xData[i]));
bottom += (lr.yData[i] - lr.yavg) * (lr.yData[i] - lr.yavg);
}
return(1 - top / bottom);
}
private void FillLinearRegressionSeria(Regression lr)
{
sLinearRegression.Points.Clear();
sRegressionInterval.Points.Clear();
sPrognosisInterval.Points.Clear();
foreach (var x in xData)
{
var regressionValue = lr.Calculate(x);
sLinearRegression.Points.Add(new DataPoint(x, regressionValue));
var regresstionIntervalBelow = lr.GetRegressionIntervalBelow(x);
var regresstionIntervalTop = lr.GetRegressionIntervalTop(x);
sRegressionInterval.Points.Add(new DataPoint(x, regresstionIntervalBelow));
sRegressionInterval.Points.Add(new DataPoint(x, regresstionIntervalTop));
var prognosisIntervalBelow = lr.GetPrognosisIntervalBelow(x);
var prognosisIntervalTop = lr.GetPrognosisIntervalTop(x);
sPrognosisInterval.Points.Add(new DataPoint(x, prognosisIntervalBelow));
sPrognosisInterval.Points.Add(new DataPoint(x, prognosisIntervalTop));
}
}
