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TrendLine Method
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dotnetCHARTING Namespace > ForecastEngine Class : TrendLine Method


Given a set of data points x[0..ndata-1],y[0..ndata-1] with individual standard deviations sig[0..ndata-1], fit them to a straight line y = a + bx by minimizing ¥ö2. Returned are a,b and their respective probable uncertainties siga and sigb, the chi-square chi2, and the goodness-of-fit probability q (that the fit would have ¥ö2 this large or larger). If mwt=0 on input, then the standard deviations are assumed to be unavailable: q is returned as 1.0 and the normalization of chi2 is to unit standard deviation on all points.

Overload List

Overload Description
TrendLine(String,Series,Series,Int32) Given a set of data points x[0..ndata-1],y[0..ndata-1] with individual standard deviations sig[0..ndata-1], fit them to a straight line y = a + bx by minimizing ¥ö2. Returned are a,b and their respective probable uncertainties siga and sigb, the chi-square chi2, and the goodness-of-fit probability q (that the fit would have ¥ö2 this large or larger). If mwt=0 on input, then the standard deviations are assumed to be unavailable: q is returned as 1.0 and the normalization of chi2 is to unit standard deviation on all points.  
TrendLine(Series,Series,Int32) Given a set of data points x[0..ndata-1],y[0..ndata-1] with individual standard deviations sig[0..ndata-1], fit them to a straight line y = a + bx by minimizing ¥ö2. Returned are a,b and their respective probable uncertainties siga and sigb, the chi-square chi2, and the goodness-of-fit probability q (that the fit would have ¥ö2 this large or larger). If mwt=0 on input, then the standard deviations are assumed to be unavailable: q is returned as 1.0 and the normalization of chi2 is to unit standard deviation on all points.  
TrendLine(SeriesCollection,Series,Int32) Given a set of data points x[0..ndata-1],y[0..ndata-1] with individual standard deviations sig[0..ndata-1], fit them to a straight line y = a + bx by minimizing ¥ö2. Returned are a,b and their respective probable uncertainties siga and sigb, the chi-square chi2, and the goodness-of-fit probability q (that the fit would have ¥ö2 this large or larger). If mwt=0 on input, then the standard deviations are assumed to be unavailable: q is returned as 1.0 and the normalization of chi2 is to unit standard deviation on all points.  

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