And when you have such a large data set, I think that you should analyze it. It's not a particularly deep topic. But it's a good data set to talk about mean, median, skewing and outliers. Not anything super interesting from a data perspective but the context may be interesting enough to capture the interest of some of your students to do basic single variable analysis. The data includes info about a player's name, salary, position, team, overall rank and I added the team rank. There are 32 teams and a bit over 50 players per team.
Analysis
Certainly some things you can do are to create some graphs. The first types that comes to mind is a dot plot, box plot and histogram. In this case the dot and box plot are provided by CODAP while the histogram comes from Google Sheets. You can see from the dot plot that the mean and median are quite separated (which we would expect from the skewing) and that there are a large number of outliers.
Since we were talking about Luke Willson, we could certainly ask how his salary compares to other NFL players (he's 455th) or other players on his team (he's 18th of 56) or even how he compares to other people the same position (21st of about 126 tight ends and is above the mean tight end salary)
Sample Questions
- Determine the mean, median and standard deviation for the salaries attribute.
- Which team has the highest mean salary? median salary?
- Choose a player of your choice, how do they compare to the league, team and position?
- Besides the way it looks, what confirms that this data is skewed to the right?
- Which team has the highest number of outliers?
Download the Data
Let me know if you used this data set or if you have suggestions of what to do with it beyond this.
I have found this data this year, and this is very old data about NFL salaries, so I will share with you the latest information about NFL player salaries.
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