Sampling from Ames, Iowa

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.

The data

In the previous lab, ``Sampling Distributions’’, we looked at the population data of houses from Ames, Iowa. Let’s start by loading that data set.

load("more/ames.RData")

In this 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.

population <- ames$Gr.Liv.Area
samp <- sample(population, 60)
  1. Describe the distribution of your sample. What would you say is the “typical” size within your sample? Also state precisely what you interpreted “typical” to mean.
summary(samp)
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##     641    1090    1294    1454    1667    2945
hist(samp, breaks = 10)

qqnorm(samp) 
qqline(samp)

It appears unimodal and right skewed. The typical size (mean) is listed above in the summary function. In this scenario, typical refers to the mean of sample.

  1. Would you expect another student’s distribution to be identical to yours? Would you expect it to be similar? Why or why not?

I would not expect the student’s distribution to be identical to mine, as the computer is randomly sampling 50 homes from the data set. Therefore, it is unlikely that another student would have the same identical sample, thus, the distribution would not be identical. However, I do expect the distribution to be more or less similar to mine as both the other student and I are drawing samples from the same data set.

Confidence intervals

One of the most common ways to describe the typical or central value of a distribution is to use the mean. In this case we can calculate the mean of the sample using,

sample_mean <- mean(samp)

Return for a moment to the question that first motivated this lab: based on this sample, what can we infer about the population? Based only on this single sample, the best estimate of the average living area of houses sold in Ames would be the sample mean, usually denoted as \(\bar{x}\) (here we’re calling it sample_mean). That serves as a good point estimate but it would be useful to also communicate how uncertain we are of that estimate. This can be captured by using a confidence interval.

We can calculate a 95% confidence interval for a sample mean by adding and subtracting 1.96 standard errors to the point estimate (See Section 4.2.3 if you are unfamiliar with this formula).

se <- sd(samp) / sqrt(60)
lower <- sample_mean - 1.96 * se
upper <- sample_mean + 1.96 * se
c(lower, upper)
## [1] 1321.416 1585.584

This is an important inference that we’ve just made: even though we don’t know what the full population looks like, we’re 95% confident that the true average size of houses in Ames lies between the values lower and upper. There are a few conditions that must be met for this interval to be valid.

  1. For the confidence interval to be valid, the sample mean must be normally distributed and have standard error \(s / \sqrt{n}\). What conditions must be met for this to be true?

The sample observations are independent. The sample size is large: n >= 30 is a good rule of thumb. The population distribution is not strongly skewed. This condition can be difficult to evaluate, so just use your best judgement.

Confidence levels

  1. What does “95% confidence” mean? If you’re not sure, see Section 4.2.2.

It means that we are 95% certain that the this range contains the actual true mean.

In this case we have the luxury of knowing the true population mean since we have data on the entire population. This value can be calculated using the following command:

mean(population)
## [1] 1499.69
  1. Does your confidence interval capture the true average size of houses in Ames? If you are working on this lab in a classroom, does your neighbor’s interval capture this value?

Taking the numbers from the above examples, the answer is YES. The confidence interval does capture the true average size of houses in Ames. (I am not working in a classroom at this time, and have no neighbors to comopare my values with.)

  1. Each student in your class should have gotten a slightly different confidence interval. What proportion of those intervals would you expect to capture the true population mean? Why? If you are working in this lab in a classroom, collect data on the intervals created by other students in the class and calculate the proportion of intervals that capture the true population mean.

I would expect that 95% of the students in the class to capture the true mean of the population. This is by the very definition of 95% confidence interval.

Using R, we’re going to recreate many samples to learn more about how sample means and confidence intervals vary from one sample to another. Loops come in handy here (If you are unfamiliar with loops, review the Sampling Distribution Lab).

Here is the rough outline:

But before we do all of this, we need to first create empty vectors where we can save the means and standard deviations that will be calculated from each sample. And while we’re at it, let’s also store the desired sample size as n.

samp_mean <- rep(NA, 50)
samp_sd <- rep(NA, 50)
n <- 60

Now we’re ready for the loop where we calculate the means and standard deviations of 50 random samples.

for(i in 1:50){
  samp <- sample(population, n) # obtain a sample of size n = 60 from the population
  samp_mean[i] <- mean(samp)    # save sample mean in ith element of samp_mean
  samp_sd[i] <- sd(samp)        # save sample sd in ith element of samp_sd
}

Lastly, we construct the confidence intervals.

lower_vector <- samp_mean - 1.96 * samp_sd / sqrt(n) 
upper_vector <- samp_mean + 1.96 * samp_sd / sqrt(n)

Lower bounds of these 50 confidence intervals are stored in lower_vector, and the upper bounds are in upper_vector. Let’s view the first interval.

c(lower_vector[1], upper_vector[1])
## [1] 1314.449 1578.351

On your own

46/50 includes the true population mean, which is 92%. While it is not exactly equal to the confidence level, it is fairly close. And especially given the 50 different samples (as opposed to a larger number i.e. 5000), this is acceptable.

An example would be 99% Confidence Interval. In this case, the critical value Z should equal 2.58. This allows us to be more confident (99%) that we are capturing the true population mean.

lower_vector99 <- samp_mean - 2.58 * samp_sd / sqrt(n) 
upper_vector99 <- samp_mean + 2.58 * samp_sd / sqrt(n)
plot_ci(lower_vector99, upper_vector99, mean(population))

48/50 includes the true population mean, which is 96%. Again, like above, it’s close, but not perfect. If we have taken more samples, we likely would get closer to 99%.

This is a product of OpenIntro that is released under a Creative Commons Attribution-ShareAlike 3.0 Unported. This lab was written for OpenIntro by Andrew Bray and Mine Çetinkaya-Rundel.