Sampling

A short description of the post.

Load the R package we will use.

library(tidyverse)
library(moderndive)

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 1180 samples of size of 26 instead of 1000 replicates of size 25 from the bowl dataset. Assign the output to virtual_samples_SEE QUIZ

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

1.b) Compute resulting SEE QUIZ replicates of proportion red

start with virtual_samples_SEE QUIZ 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 / SEE QUIZ
Assign the output to virtual_prop_red_SEE QUIZ

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_SEE QUIZ via a histogram

use labs to
label x axis = “Proportion of SEE QUIZ balls that were red”
create title = “SEE QUIZ”

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")

2.a) Take SEE QUIZ samples of size of SEE QUIZ instead of 1000 replicates of size 50. Assign the output to virtual_samples_SEE QUIZ

Segment 2: sample size = 55

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

2.b) Compute resulting SEE QUIZ replicates of proportion red

start with virtual_samples_SEE QUIZ 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 / SEE QUIZ Assign the output to virtual_prop_red_SEE QUIZ

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_SEE QUIZ via a histogram

use labs to

label x axis = “Proportion of SEE QUIZ balls that were red” create title = “SEE QUIZ”

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 = SEE QUIZ

3.a) Take SEE QUIZ samples of size of SEE QUIZ instead of 1000 replicates of size 50. Assign the output to virtual_samples_SEE QUIZ

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

3.b) Compute resulting SEE QUIZ 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_SEE QUIZ via a histogram

use labs to

label x axis = “Proportion of SEE QUIZ balls that were red” create title = “SEE QUIZ”

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 SEE QUIZ 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 0.0995

n = 55

virtual_prop_red_55  %>% 
  summarize(sd = sd(prop_red))

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0639

n = 110

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

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0435