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.
Package: statistics