library(MASS)
# conduct the regression and assign it to mod_log
# draw a scatterplot and add the regression line
# conduct the regression and assign it to mod_log
mod_log <- lm(medv ~ log(lstat), data = Boston)
# draw a scatterplot and add the regression line
plot(medv ~ log(lstat), data = Boston)
abline(mod_log, col = "red")
ex() %>% check_object("mod_log") %>% check_equal()
test_or(ex() %>% check_function("plot") %>% {
check_arg(., "formula") %>% check_equal()
check_arg(., "data") %>% check_equal()
},
ex() %>% override_solution("plot(log(Boston$lstat), Boston$medv)") %>% check_function("plot") %>% {
check_arg(., "x") %>% check_equal()
check_arg(., "y") %>% check_equal()
},
ex() %>% override_solution("plot(Boston$medv ~ log(Boston$lstat))") %>% check_function("plot") %>%
check_arg("formula") %>% check_equal()
)
ex() %>% check_function("abline") #%>% check_arg("reg") %>% check_equal()
success_msg("Correct! Although the relationship is not perfectly linear, a log transformation considerably improves our model fit and seems to be a reasonable model specification.")