sehatbetahat: Standard Errors of Estimates of Regression Coefficients (from...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Author(s) See Also Examples

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

1	sehatbetahat(X, y)

`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.

Ivan Jacob Agaloos Pesigan

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

# Simple regression------------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
X <- X[, c(1, ncol(X))]
y <- jeksterslabRdatarepo::wages.matrix[["y"]]
sehatbetahat(X = X, y = y)

# Multiple regression----------------------------------------------
X <- jeksterslabRdatarepo::wages.matrix[["X"]]
# age is removed
X <- X[, -ncol(X)]
sehatbetahat(X = X, y = y)