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