4.1.1 - StatKey Examples4.1.1 - StatKey Examples
The process of constructing a sampling distribution from a known population is the same for all types of parameters (i.e., one group proportion, one group mean, difference in two proportions, difference in two means, simple linear regression slope, and correlation). In each case we take a simple random sample of \(n\) from the population without replacement, record the sample statistic of interest, return those observations back into the population, and repeat many times. That distribution of sample statistics is known as the sampling distribution. If the sample size is large, the sampling distribution will be approximately normally with a mean equal to the population parameter.
The following pages include examples of using StatKey to construct sampling distributions for one mean and one proportion.
220.127.116.11 - NFL Salaries (One Mean)18.104.22.168 - NFL Salaries (One Mean)
In this video a sampling distribution is constructed using the "NFL Contracts (2015 in Millions)" dataset that is built into the sampling distribution for a mean feature in StatKey. This dataset includes the salaries of all 2,099 NFL players in 2015 as of the start of that season. We'll construct a sampling distribution given \(n = 5\).
22.214.171.124 - Coin Flipping (One Proportion)126.96.36.199 - Coin Flipping (One Proportion)
We are conducting an experiment in which we are flipping a fair coin 5 times and counting how many times we flip heads. Whether or not the coin lands on heads is a categorical variable with a probability of 0.50. Let's use StatKey to construct a distribution of sample proportions that we could use to determine the probability of any of the possible combinations of successes and failures.