Standard Errors of Estimates of Regression Coefficients (from \(\hat{\sigma}_{\varepsilon \ \textrm{unbiased}}^{2}\))

\(\widehat{\mathbf{se}}\) is equal to the square root of the diagonal elements of \(\widehat{\mathrm{cov}} \left( \boldsymbol{\hat{\beta}} \right)\) .

.sehatbetahat(vcovhatbetahat = NULL, X, y)

Arguments

vcovhatbetahat	k by k matrix. \(k \times k\) variance-covariance matrix of estimates of regression coefficients.
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.

Value

Returns the estimated standard errors of the estimated regression coefficients.

See also

Other standard errors of estimates of regression coefficients functions: .sehatbetahatbiased(), .sehatslopeshatprimedelta(), .sehatslopeshatprimetb(), sehatbetahatbiased(), sehatbetahat(), sehatslopeshatprimedelta(), sehatslopeshatprimetb()

Author

Ivan Jacob Agaloos Pesigan