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

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

.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.

References

Wikipedia: Linear Regression

Wikipedia: Ordinary Least Squares

See also

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

Author

Ivan Jacob Agaloos Pesigan