# 9: Sampling

Based on Chapter 7 of ModernDive. Code for Quiz 11.

1. Load the R package we will use.
``````library(tidyverse)
``````
1. 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 = SEE QUIZ

1.a) Take SEE QUIZ samples of size of SEE QUIZ instead of 1000 replicates of size 25 from the `bowl` dataset. Assign the output to virtual_samples_SEE QUIZ

``````???  <- bowl  %>%
rep_sample_n(size = ???, reps = ???)``````

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

• 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
``````??? <- ??? %>%
group_by(???) %>%
???(red = sum(color == "red")) %>%
???(prop_red = red / ???)``````

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_SEE QUIZ, aes(x = ???)) +
???(binwidth = 0.05, boundary = 0.4, color = "white") +
labs(x = "???", title = "???") ``````

Segment 2: sample size = SEE QUIZ

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

``````???  <- bowl  %>%
rep_sample_n(size = ???, reps = ???)``````

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

• 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
``````??? <- ??? %>%
group_by(???) %>%
???(red = sum(color == "red")) %>%
???(prop_red = red / ???)``````

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_SEE QUIZ, aes(x = ???)) +
???(binwidth = 0.05, boundary = 0.4, color = "white") +
labs(x = "???", title = "???") ``````

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

``````???  <- bowl  %>%
rep_sample_n(size = ???, reps = ???)``````

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

• 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
``````??? <- ??? %>%
group_by(???) %>%
???(red = sum(color == "red")) %>%
???(prop_red = red / ???)``````

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_SEE QUIZ, aes(x = ???)) +
???(binwidth = 0.05, boundary = 0.4, color = "white") +
labs(x = "???", title = "???") ``````

Calculate the standard deviations for your three sets of SEE QUIZ values of `prop_red` using the `standard deviation`

n = SEE QUIZ

``````???  %>%
summarize(sd = sd(prop_red))``````

n = SEE QUIZ

``````???  %>%
summarize(sd = sd(prop_red))``````

n = SEE QUIZ

``````???  %>%
summarize(sd = sd(prop_red))``````

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