Except for a few twists that I’ll tell you about in a moment, the population data are structurally identical to the raises data you worked with earlier. Specifically:

• Each row represents a county, just like each row represented a department head in the “Raises” dataset.
• The first column names a county, just like the “Raises” dataset’s first row named a department head.
• The second column gives each county’s “old” (2010) population figure, just like the second column in the “Raises” dataset gave each department head’s old (pre-raise) salary.
• The third column gives each county’s “new” (latest) population, just like the third column in the “Raises” dataset gave each department head’s new (post-raise) salary.

About those twists, though:

• In the “Raises” dataset, every “New salary” figure was larger than its corresponding “Old salary” figure, because each department head was getting  raise, even if only a minimal one. Nobody’s salary got reduced. In the population dataset, however, it is entirely possible for some of the “new” estimates to be smaller than the “old” estimates, because populations can shrink if enough people move away. This characteristic of the dataset means that if you calculate changes and percent changes from 2010 to now, some will be negative. That’s OK; when a county’s population shrinks, any measure of the change that has occurred should be negative.
• The “Percent of the whole” analysis shown in the salary figures demonstration would be tricky to implement on the population data if any of the counties saw declines in their population. If you wanted to use something like it, you’d have to divide the counties into those that gained population and those that lost population. For the counties that gained population, you could use the =sum function to calculate the total population gain, then show the percentage of that gain that occurred in each county that had a gain. You could do the same analysis for counties that lost population.
• The population data contains a fourth column, “Region,” that indicates which region of Tennessee – West, Middle, or East – each county falls within. With just a little creative adaptation of the skills you’ve learned, you could examine population change differences not only among individual counties but also among these three grand divisions. If you’re working on this for a class, maybe your professor will give you some bonus points for figuring out how to do so – or at least trying.