library(MASS)
mod_pl <- lm(medv ~ poly(log(lstat), 2, raw = T), data = Boston)
# set up the relevant data points
# predict the corresponding values of medv
# compute the expected effect of the change
# set up the relevant data points
new_data <- data.frame(lstat = c(10, 11))
# predict the corresponding values of medv
Y_hat <- predict(mod_pl, newdata = new_data)
# compute the expected effect of the change
diff(Y_hat)
ex() %>% check_object("new_data") %>% check_equal()
ex() %>% check_object("Y_hat") %>% check_equal()
ex() %>% check_function("diff") %>% check_result() %>% check_equal()
success_msg("Correct! The expected change in medv for an increase in lstat from 10% to 11% is about -1.16 (that is -1160$).")