Code for Quiz 10.
1.Load the R packages we will use.
-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
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 = 1200)
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
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")
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 = 1200)
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
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")
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 = 1200)
3.b) Compute resulting 1120 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
3.c) Plot distribution of virtual_prop_red_120 via a histogram
use labs to
-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 1200 values of prop_red using the standard deviation
n = 30
n = 55
n = 120
The distribution with sample size, n = 120, has the smallest standard deviation (spread) around the estimated proportion of red balls.