dot-vcovhatbetahatbiased: Variance-Covariance Matrix of Estimates of Regression...

In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description

Variance-Covariance Matrix of Estimates of Regression Coefficients (from \hat{σ}_{\varepsilon \ \textrm{biased}}^{2})

Usage

.vcovhatbetahatbiased(sigma2hatepsilonhatbiased = NULL, X, y)

Arguments

sigma2hatepsilonhatbiased	Numeric. Biased estimate of error variance.
X	n by k numeric matrix. The data matrix \mathbf{X} (also known as design matrix, model matrix or regressor matrix) is an n \times k matrix of n observations of k regressors, which includes a regressor whose value is 1 for each observation on the first column.
y	Numeric vector of length n or n by 1 matrix. The vector \mathbf{y} is an n \times 1 vector of observations on the regressand variable.

Author(s)

Ivan Jacob Agaloos Pesigan

References

Wikipedia: Linear Regression

Wikipedia: Ordinary Least Squares

See Also

Other variance-covariance of estimates of regression coefficients functions: .vcovhatbetahat(), vcovhatbetahatbiased(), vcovhatbetahat()

jeksterslabds/jeksterslabRlinreg documentation built on Jan. 7, 2021, 8:30 a.m.