How to calculate the 95% confidence interval for the slope in a linear regression model in R
Let's fit the model:
> library(ISwR)> fit <- lm(metabolic.rate ~ body.weight, rmr)> summary(fit)Call:lm(formula = metabolic.rate ~ body.weight, data = rmr)Residuals: Min 1Q Median 3Q Max -245.74 -113.99 -32.05 104.96 484.81 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 811.2267 76.9755 10.539 2.29e-13 ***body.weight 7.0595 0.9776 7.221 7.03e-09 ***---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 157.9 on 42 degrees of freedomMultiple R-squared: 0.5539, Adjusted R-squared: 0.5433 F-statistic: 52.15 on 1 and 42 DF, p-value: 7.025e-09 The 95% confidence interval for the slope is the estimated coefficient (7.0595) ± two standard errors (0.9776).
This can be computed using confint:
> confint(fit, 'body.weight', level=0.95) 2.5 % 97.5 %body.weight 5.086656 9.0324