Regression Coefficients Hypothesis Test and Confidence Intervals

Regression Coefficients Hypothesis Test and Confidence Intervals

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

Arguments

betahat	Numeric vector of length k or k by 1 matrix. The vector \(\boldsymbol{\hat{\beta}}\) 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.

See also

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

Author

Ivan Jacob Agaloos Pesigan