regress
               Multiple Linear Regression using Least Squares Fit of y on X
 with the model y = X * beta + e.
Here,
y is a column vector of observed values
 X is a matrix of regressors, with the first column filled with
 the constant value 1
 beta is a column vector of regression parameters
 e is a column vector of random errors
 Arguments are
y in the model
 X in the model
 Return values are
beta in the model
  r and rint can be passed to rcoplot to visualize
 the residual intervals and identify outliers.
NaN values in y and X are removed before calculation begins.
See also: regress_gp, regression_ftest, regression_ttest
Source Code: regress