/// <summary>
/// compute the "a" factor of the linear function of design2
/// </summary>
/// <param name="design1"></param>
/// <param name="design2"></param>
/// <returns></returns>
public static double a(NuGenStatistics design1, NuGenStatistics design2)
{
try
{
return(cov(design1, design2) / (Math.Pow(design2.s(), 2)));
}
catch (Exception)
{
return(double.NaN);
}
}
/// <summary>
/// compute the "a" factor of the linear function of design
/// </summary>
/// <param name="design"></param>
/// <returns></returns>
public double a(NuGenStatistics design)
{
try
{
return(this.cov(design) / (Math.Pow(design.s(), 2)));
}
catch (Exception)
{
return(double.NaN);
}
}
/// <summary>
/// compute the correlation coefficient
/// </summary>
/// <param name="design1"></param>
/// <param name="design2"></param>
/// <returns></returns>
public static double r(NuGenStatistics design1, NuGenStatistics design2)
{
try
{
return(cov(design1, design2) / (design1.s() * design2.s()));
}
catch (Exception)
{
return(double.NaN);
}
}
/// <summary>
/// compute the correlation coefficient
/// </summary>
/// <param name="design"></param>
/// <returns></returns>
public double r(NuGenStatistics design)
{
try
{
return(this.cov(design) / (this.s() * design.s()));
}
catch (Exception)
{
return(double.NaN);
}
}
/// <summary>
/// compute the covariance
/// </summary>
/// <param name="s1"></param>
/// <param name="s2"></param>
/// <returns></returns>
public static double cov(NuGenStatistics s1, NuGenStatistics s2)
{
try
{
if (s1.length() != s2.length())
{
return(double.NaN);
}
int len = s1.length();
double sum_mul = 0;
for (int i = 0; i <= len - 1; i++)
{
sum_mul += (s1.list[i] * s2.list[i]);
}
return((sum_mul - len * s1.mean() * s2.mean()) / (len - 1));
}
catch (Exception)
{
return(double.NaN);
}
}
/// <summary>
/// compute the "b" factor of the linear function of design2
/// </summary>
/// <param name="design1"></param>
/// <param name="design2"></param>
/// <returns></returns>
public static double b(NuGenStatistics design1, NuGenStatistics design2)
{
return(design1.mean() - a(design1, design2) * design2.mean());
}
/// <summary>
/// compute the "b" factor of the linear function of design
/// </summary>
/// <param name="design"></param>
/// <returns></returns>
public double b(NuGenStatistics design)
{
return(this.mean() - this.a(design) * design.mean());
}