dot-betahatinference: Regression Coefficients Hypothesis Test and Confidence...

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

Description

Regression Coefficients Hypothesis Test and Confidence Intervals

Usage

.betahatinference(betahat = NULL, sehatbetahat = NULL, n, X, y)

Arguments

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.
sehatbetahat	Numeric vector. Standard errors of regression coefficients.
n	Integer. Sample size.
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 a matrix with the following columns

coef

Coefficients.

se

Standard error.

t

t-statistic.

p

p-value.

ci_0.05

Lower limit 99.99% confidence interval.

ci_0.5

Lower limit 99% confidence interval.

ci_2.5

Lower limit 95% confidence interval.

ci_97.5

Upper limit 95% confidence interval.

ci_99.5

Upper limit 99% confidence interval.

ci_99.95

Upper limit 99.99% confidence interval.

Author(s)

Ivan Jacob Agaloos Pesigan

See Also

Other inference functions: .slopeshatprimeinference(), betahatinference(), slopeshatprimeinference()

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