Load the R package we will use.

library(tidyverse)
library(moderndive) #install before loading

Quiz questions

Replace all the instances of ‘SEE QUIZ’. These are inputs from your moodle quiz.
Replace all the instances of ‘???’. These are answers on your moodle quiz.
Run all the individual code chunks to make sure the answers in this file correspond with your quiz answers
After you check all your code chunks run then you can knit it. It won’t knit until the ??? are replaced
The quiz assumes that you have watched the videos and worked through the examples in Chapter 7 of ModernDive

Question:

7.2.4 in Modern Dive with different sample sizes and repetitions - Make sure you have installed and loaded the tidyverse and the moderndive packages - Fill in the blanks - Put the command you use in the Rchunks in your Rmd file for this quiz.

Modify the code for comparing differnet sample sizes from the virtual bowl

Segment 1: sample size = 26

1.a) Take 1100 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 = 1100)

1.b) Compute resulting 1100 replicates of proportion red 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 - label x axis = “Proportion of 26 balls that were red” - create title = “26”

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 = 57 2.a) Take 1100 samples of size of 57 instead of 1000 replicates of size 50. Assign the output to virtual_samples_57

virtual_samples_57  <- bowl  %>% 
rep_sample_n(size = 57, reps = 1100)

2.b) Compute resulting 1100 replicates of proportion red

start with virtual_samples_57 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 / 57
Assign the output to virtual_prop_red_57

virtual_prop_red_57 <- virtual_samples_57 %>% 
  group_by(replicate) %>% 
  summarize(red = sum(color == "red")) %>% 
  mutate(prop_red = red / 57)

2.c) Plot distribution of virtual_prop_red_57 via a histogram use labs to - label x axis = “Proportion of 57 balls that were red” - create title = “57”

ggplot(virtual_prop_red_57, aes(x = prop_red)) +
  geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 57 balls that were red", title = "57")

Segment 3: sample size = 110 3.a) Take 1100 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 = 1100)

3.b) Compute resulting 1100 replicates of proportion red - start with virtual_samples_110 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 / 110 - Assign the output to virtual_prop_red_110

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 - label x axis = “Proportion of 110 balls that were red” - create title = “110”

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 1100 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 0.0936

n = 57

virtual_prop_red_57  %>% 
  summarize(sd = sd(prop_red))

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0645

n = 110

virtual_prop_red_110  %>% 
  summarize(sd = sd(prop_red))

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0438

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

Sampling

Question: