Chapter 10

library(tidyverse) library(openintro) library(statsr) library(broom)

Exercise 1

dim(hfi)

Exercise 2

hfi_2016 <- hfi %>% filter(year == 2016) %>% select(year, hf_score, pf_score, pf_expression_control) hfi_2016

Exercise 3

ggplot(hfi_2016, aes(pf_expression_control, pf_score)) + geom_point(color = “purple”) + geom_smooth(method = ‘lm’, se = TRUE) + xlab(“Expression Control”) + ylab (“Pf Score”) hfi_2016 %>% summarize(cor(pf_expression_control, pf_score))

Exercise 4

plot_ss(x = pf_expression_control, y = pf_score, data = hfi_2016) plot_ss(x = pf_expression_control, y = pf_score, data = hfi_2016, showSquares = TRUE)

Exercise 5

plot_ss(x = pf_expression_control, y = pf_score, data = hfi_2016)

Exercise 6

exercise_5 <- lm(hf_score ~ pf_expression_control, data = hfi_2016) summary(exercise_5)

ggplot(data = hfi_2016, aes(x = pf_expression_control, y = pf_score)) + geom_point() + geom_smooth(method = “lm”, se = FALSE)

Exercise 7

pf_score1 = 4.28 + 0.542 * 3 pf_score1 pf_expression_control1 <- hfi %>% group_by(pf_score) %>% filter(pf_expression_control == 3) %>% select(pf_score, pf_expression_control) pf_expression_control1

m1_aug <- augment(m1) ggplot(data = m1_aug, aes(x = .fitted, y = .resid)) + geom_point() + geom_hline(yintercept = 0, linetype = “dashed”, color = “red”) + xlab(“Fitted values”) + ylab(“Residuals”)

Exercise 8

ggplot(data = m1_aug, aes(x = .resid)) + geom_histogram(binwidth = 0.25) + xlab(“Residuals”)

Exercise 9

qqnorm(m1_aug\(.resid) qqline(m1_aug\).resid)