Function MannWhitneyU(sample1 As Range, sample2 As Range)
    Dim n1 As Double
    Dim n2 As Double
    Dim u1 As Double
    Dim q As Double
    n1 = WorksheetFunction.Count(sample1)
    n2 = WorksheetFunction.Count(sample2)
    For Each v1 In sample1
        If WorksheetFunction.IsNumber(v1) Then
            u1 = u1 + WorksheetFunction.CountIf(sample2, Replace("<" & v1, ",", ".")) _
                    + WorksheetFunction.CountIf(sample2, Replace("=" & v1, ",", ".")) / 2
        End If
    Next v1
    MannWhitneyU = (u1 - n1 * n2 / 2) / Sqr(n1 * n2 * (n1 + n2 + 1) / 12)
End Function

Function MannWhitneyU(sample1 As Range, sample2 As Range)
    Dim n1 As Integer
    Dim n2 As Integer
    n1 = WorksheetFunction.Count(sample1)
    n2 = WorksheetFunction.Count(sample2)
    MannWhitneyU = n1 * n2 * (n1 + n2 + 1)
End Function

<DnaLibrary RuntimeVersion="v4.0" Name="Simple Statistics">
  <Project Language="CS">
    <Reference Path="MathNet.Numerics.dll" />
    <![CDATA[
using System;
using ExcelDna.Integration;
using MathNet.Numerics.LinearAlgebra.Double;

namespace SimpleStatistics
{
    public class LogisticRegression
    {
        public static object EstimateLogisticRegression(double[] dependent, double[,] predictor)
        {
            var n = dependent.Length;
            var dim = predictor.GetLength(1) + 1;
            var x = new DenseMatrix(n, 1, 1).Append(new DenseMatrix(predictor));
            var y = new DenseVector(dependent);
            var w = new DiagonalMatrix(n);
            var b = new DenseVector(dim);
            var b2 = new DenseVector(dim);
            var p = new DenseVector(n);
            for (int k = 0; k < 100; k++)
            {
                for (int i = 0; i < n; i++) p[i] = 1 / (1 + Math.Exp(-b * x.Row(i)));
                w.SetDiagonal(p.PointwiseMultiply(-p.Subtract(1)));
                var xtw = x.TransposeThisAndMultiply(w);
                b2 = (DenseVector)((xtw * x).Inverse() * xtw * (x * b + w.Inverse() * (y - p)));
                var precision = (b2 - b).AbsoluteMaximum();
                if (precision <= 0.000001) return (double[])b2;
                b = b2;
            }
            return ExcelError.ExcelErrorNA;
        }
    }
}
    ]]>
  </Project>
</DnaLibrary>