Population & sampling distribution means
One of the useful features of sampling distributions is that you can quantify them. In particular, you can calculate summary statistics on them. Here, we'll look at the relationship between the mean of the sampling distribution and the population parameter that the sampling is supposed to estimate.
Three sampling distributions are provided. In each case, the employee attrition dataset was sampled using simple random sampling, then the mean attrition was calculated. This was done 1000 times to get a sampling distribution of mean attritions. One sampling distribution used a sample size of 5 for each replicate, one used 50, and one used 500.
attrition_pop, sampling_distribution_5, sampling_distribution_50, and sampling_distribution_500 are available; dplyr is loaded.
This exercise is part of the course
Sampling in R
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the mean across replicates of the mean attritions in sampling_distribution_5
mean_of_means_5 <- sampling_distribution_5 %>%
___
# Do the same for sampling_distribution_50
mean_of_means_50 <- ___
# ... and for sampling_distribution_500
mean_of_means_500 <- ___
# See the results
mean_of_means_5
mean_of_means_50
mean_of_means_500