betahatinference: Regression Coefficients Hypothesis Test and Confidence...
In jeksterslabds/jeksterslabRlinreg: Jekster's Lab Linear Regression Utilities

Description Usage Arguments Value Author(s) See Also Examples

Regression Coefficients Hypothesis Test and Confidence Intervals

1	betahatinference(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.

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.

Ivan Jacob Agaloos Pesigan

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

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

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