Here's a guess as to what the problem is asking for.

You have a lot of data saying what each player's salary is, and how many home runs they've hit.

A simple model would say that the more home runs the player hit, the higher the salary they should get. For example,

S = HR (HR = # home runs, S = salary in $1000)

would say that for each home run they hit, they get $1000. So a person who hit 5 home runs would get $5000, a person who hit 8 home runs gets $8000, etc.

You might have a more sophisticated model, e.g.,

S = $5000 + 2000($/HR)*HR.

So they all get a base salary, and for every home run they hit, get an extra $2000. In this model, a person who hit no home runs gets $5000, and a person who hits 7 home runs gets $19000.

Instead of making the model first, and then calculating salaries, we could start off with the data you have (try INSERT CHART in Excel) and figure out if there's a straight line through the data. If there is, it will give you a model of the form:

S = A + B*HR

Your A and B are regression coefficients. In the model of the previous paragraph, A = $5000 and B = $2000/HR.

Most real world data doesn't come on a nice straight line. Instead, there's a small difference between each data point and the nice straight line. We put all of this in a fudge factor called the error on each data point, possibly called Ei (e sub i). Then your model would be:

S = A + B*HR + Ei

and of course it would fit perfectly because the Ei takes care of all the inaccuracies.

So hopefully this matches the model your professor is asking you to use.