dot-Xbetahat: y-hat <=ft( \mathbf{\hat{y}} = \mathbf{X}...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Calculates y-hat ≤ft( \mathbf{\hat{y}} \right), that is, the predicted value of \mathbf{y} given \mathbf{X} using

\mathbf{\hat{y}} = \mathbf{X} \boldsymbol{\hat{β}}

where

\boldsymbol{\hat{β}} = ≤ft( \mathbf{X}^{T} \mathbf{X} \right)^{-1} ≤ft( \mathbf{X}^{T} \mathbf{y} \right) .

1	.Xbetahat(X, betahat = NULL, y = NULL)

`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.
`betahat`	Numeric vector of length `k` or `k` by `1` matrix. The vector \boldsymbol{\hat{β}} is a k \times 1 vector of estimates of k unknown regression coefficients.
`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.