Computes a slope and its standard error for a simple linear regression
model (random-x model) using the estimated Pearson correlation and the
estimated standard deviations of the response variable and predictor
variable. This function is useful in a meta-analysis of slopes of a
simple linear regression model where some studies report the Pearson
correlation but not the slope.
Usage
se.slope(cor, sdy, sdx, n)
Arguments
- cor
estimated Pearson correlation
- sdy
estimated standard deviation of the response variable
- sdx
estimated standard deviation of the predictor variable
- n
sample size
Value
Returns a one-row matrix:
References
Snedecor GW, Cochran WG (1980).
Statistical methods, 7th edition.
ISU University Pres, Ames, Iowa.
Examples
se.slope(.392, 4.54, 2.89, 60)
#> Estimate SE
#> Slope: 0.6158062 0.1897647
# Should return:
# Estimate SE
# Slope: 0.6158062 0.1897647