Population & sampling distribution means
One of the useful features of sampling distributions is that you can quantify them. Specifically, you can calculate summary statistics on them. Here, you'll look at the relationship between the mean of the sampling distribution and the population parameter's mean.
Three sampling distributions are provided. For each, 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; numpy as np is loaded.
This exercise is part of the course
Sampling in Python
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# Calculate the mean of the mean attritions for each sampling distribution
mean_of_means_5 = ____
mean_of_means_50 = ____
mean_of_means_500 = ____
# Print the results
print(mean_of_means_5)
print(mean_of_means_50)
print(mean_of_means_500)