Does anyone know of an efficient way to do multiple linear regression in C#, where the number of simultaneous equations may be in the 1000's (with 3 or 4 different inputs). After reading this article on multiple linear regression I tried implementing it with a matrix equation: Matrix y = new Matrix( new double[,]{{745}, {895}, {442}, {440}, {1598}}); Matrix x = new Matrix( new double[,]{{1, 36, 66}, {1, 37, 68}, {1, 47, 64}, {1, 32, 53}, {1, 1, 101}}); Matrix b = (x.Transpose() * x).Inverse() * x.Transpose() * y; for (int i = 0; i < b.Rows; i++) { Trace.WriteLine("INFO: " + b[i, 0].ToDouble()); } However it does not scale well to the scale of 1000's of equations due to the matrix inversion operation. I can call the R language and use that, however I was hoping there would be a pure .Net solution which will scale to these large sets. Any suggestions? EDIT #1: I have settled using R for the time being. By using statconn (downloaded here) I have found it to be both fast & relatively easy to use this method. I.e. here is a small code snippet, it really isn't much code at all to use the R statconn library (note: this is not all the code!). _StatConn.EvaluateNoReturn(string.Format("output <- lm({0})", equation)); object intercept = _StatConn.Evaluate("coefficients(output)['(Intercept)']"); parameters[0] = (double)intercept; for (int i = 0; i < xColCount; i++) { object parameter = _StatConn.Evaluate(string.Format("coefficients(output)['x{0}']", i)); parameters[i + 1] = (double)parameter; }