One sample from Ames, Iowa
Welcome to Part B! In this part, we'll translate that concept of reliability of mean estimates into something more tangible: confidence intervals.
If you have access to data on an entire population, say the size of every house in Ames, Iowa, it's straight forward to answer questions like, “How big is the typical house in Ames?” and “How much variation is there in sizes of houses?”. If you have access to only a sample of the population, as is often the case, the task becomes more complicated.
What is your best guess for the typical size if you only know the sizes of several dozen houses? This sort of situation requires that you use your sample to make inference on what your population looks like.
In this second part of the lab we'll start with a simple random sample of size 60 from the population. Specifically, this is a simple random sample of size 60. Note that the data set has information on many housing variables, but for the first portion of the lab we'll focus on the size of the house, represented by the variable Gr.Liv.Area.
This exercise is part of the course
Data Analysis and Statistical Inference
Exercise instructions
- Draw a sample of size 60 from
population. Assign it tosamp. - Calculate the mean of your sample. Assign it to
sample_mean. - Draw a histogram of the sample.
Hands-on interactive exercise
Have a go at this exercise by completing this sample code.
# The ames data frame is already loaded into the workspace
# Take a sample of size 60 of the population:
population <- ames$Gr.Liv.Area
samp <-
# Calculate the mean:
sample_mean <-
# Draw a histogram: