Bootstrapping

Based on Chapter 8 of ModernDive. Code for Quiz 12.

  1. Load the R package we will use.
  1. Quiz questions

Question 7.2.4 in Modern Dive with different sample sizes and repetitions

Segment 1: sample size = 26

1.a) Take 1180 samples of size of 26 instead of 1000 replicates of size 25 from the bowl dataset. Assign the output to virtual_samples_26

virtual_samples_26 <- bowl  %>% 
rep_sample_n(size = 26, reps = 1180)

1.b) Compute resulting 1180 replicates of proportion red start with virtual_samples_26 THEN group_by replicate THEN create variable red equal to the sum of all the red balls create variable prop_red equal to variable red / 26 Assign the output to virtual_prop_red_26

virtual_prop_red_26 <- virtual_samples_26 %>% 
  group_by(replicate) %>% 
  summarize(red = sum(color == "red")) %>% 
  mutate(prop_red = red / 26)

1.c) Plot distribution of virtual_prop_red_26 via a histogram use labs to

ggplot(virtual_prop_red_26, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 26 balls that were red", title = "26")


Segment 2: sample size = 55

2.a) Take 1180 samples of size of 55 instead of 1000 replicates of size 50. Assign the output to virtual_samples_55

virtual_samples_55 <- bowl  %>% 
rep_sample_n(size = 55, reps = 1180)

2.b) Compute resulting 1180 replicates of proportion red

virtual_prop_red_55<- virtual_samples_55  %>% 
  group_by(replicate) %>% 
  summarize(red = sum(color == "red")) %>% 
  mutate(prop_red = red / 55)

2.c) Plot distribution of virtual_prop_red_55 via a histogram use labs to

ggplot(virtual_prop_red_55, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 55 balls that were red", title = "55") 


Segment 3: sample size = 110

3.a) Take 1180 samples of size of 110 instead of 1000 replicates of size 50. Assign the output to virtual_samples_110

virtual_samples_110 <- bowl  %>% 
rep_sample_n(size = 110, reps = 1180)

3.b) Compute resulting 1180 replicates of proportion red

virtual_prop_red_110 <- virtual_samples_110 %>% 
  group_by(replicate) %>% 
  summarize(red = sum(color == "red")) %>% 
  mutate(prop_red = red / 110)

3.c) Plot distribution of virtual_prop_red_110 via a histogram use labs to

ggplot(virtual_prop_red_110, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 110 balls that were red", title = "110")


Calculate the standard deviations for your three sets of 1180 values of prop_red using the standard deviation n = 26

virtual_prop_red_26 %>% 
  summarize(sd = sd(prop_red))
# A tibble: 1 x 1
      sd
   <dbl>
1 0.0904

n = 55

virtual_prop_red_55 %>% 
  summarize(sd = sd(prop_red))
# A tibble: 1 x 1
      sd
   <dbl>
1 0.0647

n=110

virtual_prop_red_110 %>% 
  summarize(sd = sd(prop_red))
# A tibble: 1 x 1
      sd
   <dbl>
1 0.0449

The distribution with sample size, n = 110, has the smallest standard deviation (spread) around the estimated proportion of red balls.