sampling

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

1. Load the packages we will use

library(tidyverse)
library(moderndive)

2. 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

Questions:

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= 30

1.a) Take 1200 samples of size of 30 instead of 1000 replicates of size 25 from the bowl dataset. Assign the output to virtual_samples_30

virtual_samples_30 <- bowl  %>% 
rep_sample_n(size = 30, reps = 1000)

1.b) Compute resulting 1200 replicates of proportion red

start with virtual_samples_30 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 / 30

Assign the output to virtual_prop_red_30

 virtual_prop_red_30 <- virtual_samples_30 %>% 
  group_by(replicate) %>% 
   summarize(red = sum(color == "red")) %>% 
   mutate(prop_red = red /30)

1.c) Plot distribution of virtual_prop_red_30 via a histogram

use labs to

label x axis = “Proportion of 30 balls that were red” create title = “30”

ggplot(virtual_prop_red_30, aes(x =prop_red)) +
    geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 30 balls that were red", title = "30") 

Segment 2: sample size=55

2.a) Take 1200 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 = 1000)

2.b) Compute resulting 1200 replicates of proportion red

start with virtual_samples_55 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 / 55

Assign the output to virtual_prop_red_55

 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

label x axis = “Proportion of 55 balls that were red”

create title = “55”

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 = 120

3.a) Take 1200 samples of size of 120 instead of 1000 replicates of size 50. Assign the output to virtual_samples_120

virtual_samples_120 <- bowl  %>% 
rep_sample_n(size=120,reps = 1000)

3.b) Compute resulting 1200 replicates of proportion red

start with virtual_samples_120 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 / 120

Assign the output to virtual_prop_red_120

virtual_prop_red_120<- virtual_samples_120 %>% 
  group_by(replicate) %>% 
  summarize(red = sum(color == "red")) %>% 
  mutate(prop_red = red / 120)

3.c) Plot distribution of virtual_prop_red_120 via a histogram

label x axis = “Proportion of 120 balls that were red”

create title = “120”

ggplot(virtual_prop_red_120, aes(x =prop_red)) +
   geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
  labs(x = "Proportion of 120 balls that were red", title = "120") 

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

Calculate the standard deviations for your three sets of 1200 values of prop_red using the standard deviation

n=30

virtual_prop_red_30 %>% 
  summarize(sd = sd(prop_red))

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0872

n=55

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

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0638

n=120

virtual_prop_red_120 %>% 
  summarize(sd = sd(prop_red))

# A tibble: 1 x 1
      sd
   <dbl>
1 0.0429