Simple C# Linear Regression Class

LinearRegression.cs

// Based on https://gist.github.com/NikolayIT/d86118a3a0cb3f5ed63d674a350d75f2
// With some tweaks
// This file is licensed under MIT license

public class LinearRegression
{
    public static double MinimumLength { get; } = 3;
    public double Average { get; private set; } = 0;
    public double R2 { get; private set; } = 1;
    public double Slope { get; private set; } = 0;
    public double StdDev { get; private set; } = 0;
    public double ResidualStdError { get; private set; } = double.NaN;
    public double YIntercept { get; private set; } = 0;
    public double DegreesOfFreedom { get; private set; } = 0;

    /// <summary>
    /// Set values and calculate the results for this linear regression
    /// </summary>
    /// <param name="xs">Known x values, must be the same length as ys</param>
    /// <param name="ys">Known y values, must be the same length as xs</param>
    /// <param name="useSecondPass">Set to true (default) if you wish to execute the second pass, 
    /// this is required by <see cref="ResidualStdError"/>. But can be slow on large datasets</param>
    public void SetValues(double[] xs, double[] ys, bool useSecondPass = true)
    {
        this.Average = 0;
        this.R2 = 1;
        this.Slope = 0;
        this.StdDev = 0;
        this.ResidualStdError = double.NaN;
        this.YIntercept = 0;

        if (xs.Length != ys.Length || xs.Length < MinimumLength)
            return;

        // First pass to calculate base Linear Regression parameters
        double sumX = 0;
        double sumY = 0;
        double sumXsq = 0;
        double sumYsq = 0;
        double codeviates = 0;

        int count = xs.Length;

        for (int i = 0; i < count; i++)
        {
            double x = xs[i];
            double y = ys[i];
            codeviates += x * y;
            sumX += x;
            sumY += y;
            sumXsq += x * x;
            sumYsq += y * y;
        }

        double averageX = sumX / count;
        double averageY = sumY / count;

        double sumSquaresX = sumXsq - (averageX * averageX * count);
        double sumSquaresY = sumYsq - (averageY * averageY * count);

        double rNumerator = (count * codeviates) - (sumX * sumY);
        double rDenominator = ((count * sumXsq) - (sumX * sumX)) * ((count * sumYsq) - (sumY * sumY));
        double R = rNumerator / Math.Sqrt(rDenominator);
        double correlation = codeviates - (averageX * averageY * count);

        this.Average = averageY;
        this.R2 = R * R;
        this.Slope = correlation / sumSquaresX;
        this.StdDev = Math.Sqrt(sumSquaresY / (count - 1));
        this.ResidualStdError = Math.Sqrt(sumSquaresY / (count - 1));
        this.YIntercept = averageY - (this.Slope * averageX);
        this.DegreesOfFreedom = count - 2;

        // Second pass to calculate residual error. Can be skiped.
        if (!useSecondPass)
            return;

        double sumDistanceSq = 0;

        for (int i = 0; i < count; i++)
        {
            double x = xs[i];
            double y = ys[i];
            double yPredict = (x * this.Slope) + this.YIntercept;
            double distance = y - yPredict;
            sumDistanceSq += distance * distance;
        }

        this.ResidualStdError = Math.Sqrt(sumDistanceSq / this.DegreesOfFreedom);
    }
}