Sampling distribution of prices
And now the real challenge: running a loop.
Since you have access to the population, we can simulate the sampling distribution for \(\overline{x}\) by taking 5000 samples of size 50 from the population and compute 5000 sample means.
This exercise is part of the course
Data Analysis and Statistical Inference
Exercise instructions
- Initialize an object
sample_means50with 5000NAs. - Use a for loop to create 5000 samples of size 50 of
price. - Inside the loop, calculate the mean of each sample and assign it to its place in
sample_means50. - Inspect the result by printing
sample_means50using thehead()function. - Based on this sampling distribution, think about what the mean home price of the population will be?
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# The ames data frame and area and price objects are already loaded into the workspace
# Initialize sample_means50
sample_means <-
# Code the for loop
for (i in ___) {
}
# Print out the head of sample_means50