Even more on sampling

Note that we're able to use a single for loop to build two distributions by adding additional lines inside the curly braces.

Don't worry about the fact that samp is used for the name of two different objects. In the second command of the for loop, the mean of samp is saved to the relevant place in the vector sample_means10.

With the mean saved, we're now free to overwrite the object samp with a new sample, this time of size 50

In general, anytime you create an object using a name that is already in use, the old object will get replaced with the new one, i.e. R will write over the existing object with the new one, which is something you want to be careful about if you don't intend to do so.

This exercise is part of the course

Data Analysis and Statistical Inference

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Exercise instructions

Inspect the code and execute it.

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
set.seed(12345)

# Initialize the sample distributions:
sample_means10 <- rep(NA, 5000)
sample_means50 <- rep(NA, 5000)
sample_means100 <- rep(NA, 5000)

# Run the for loop:
for (i in 1:5000) {
	samp <- sample(area, 10)
	sample_means10[i] <- mean(samp)
	samp <- sample(area, 50)
	sample_means50[i] <- mean(samp)
	samp <- sample(area, 100)
	sample_means100[i] <- mean(samp)
}

# Take a look at the results:
head(sample_means10)
head(sample_means50)
head(sample_means100)

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