Page 346 - Applied Statistics with R

346 CHAPTER 14. TRANSFORMATIONS

set.seed(314)
quad_data = sim_quad(sample_size = 200)

lin_fit = lm(y ~ x, data = quad_data)
summary(lin_fit)

##
## Call:
## lm(formula = y ~ x, data = quad_data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -20.363 -7.550 -3.416 8.472 26.181
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -18.3271 1.5494 -11.83 <2e-16 ***
## x 24.8716 0.5343 46.55 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 10.79 on 198 degrees of freedom
## Multiple R-squared: 0.9163, Adjusted R-squared: 0.9158
## F-statistic: 2167 on 1 and 198 DF, p-value: < 2.2e-16

plot(y ~ x, data = quad_data, col = "grey", pch = 20, cex = 1.5,
main = "Simulated Quadratic Data")
abline(lin_fit, col = "darkorange", lwd = 2)