Based on Chapter 7 of ModernDive. Code for Quiz 11.
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
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 1120 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 = 1120)
1.b) Compute resulting 1120 replicates of proportion red
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
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 1120 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 =1120)
2.b) Compute resulting 1120 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 = 114
3.a) Take 1120 samples of size of 114 instead of 1000 replicates of size 50. Assign the output to virtual_samples_114
virtual_samples_114 <- bowl %>%
rep_sample_n(size= 114, reps = 1120)
3.b) Compute resulting 1120 replicates of proportion red
virtual_prop_red_114 <- virtual_samples_114 %>%
group_by(replicate) %>%
summarize(red = sum(color == "red")) %>%
mutate(prop_red = red / 114)
3.c) Plot distribution of virtual_prop_red_114 via a histogram use labs to
ggplot(virtual_prop_red_114, aes(x = prop_red)) +
geom_histogram(binwidth = 0.05, boundary = 0.4, color = "white") +
labs(x = "Proportion of 114 balls that were red", title = "114")
Calculate the standard deviations for your three sets of 1120 values of prop_red using the standard deviation
n = 30virtual_prop_red_30 %>%
summarize(sd = sd(prop_red))
# A tibble: 1 x 1
sd
<dbl>
1 0.0870
virtual_prop_red_55 %>%
summarize(sd = sd(prop_red))
# A tibble: 1 x 1
sd
<dbl>
1 0.0642
virtual_prop_red_114 %>%
summarize(sd = sd(prop_red))
# A tibble: 1 x 1
sd
<dbl>
1 0.0417
The distribution with sample size, n = 114, has the smallest standard deviation (spread) around the estimated proportion of red balls.