Correct

In the previous problem, the most likely grade assigned is a C, so that is your best guess at this point for the grade earned by a randomly selected student. [Note: our best guess is not always the average, unless we are minimizing the sum of squared errors.]

So far, this problem has nothing to do with regression analysis. Regression analysis always involves at least two variables (like correlation). The relationship between the two variables allows us to improve our prediction over and above what we can learn from knowing the average score.

Imagine that the professor says the following: "Over the past five years, I have collected information about attendance and grades. I find that there is a positive correlation between points and attendance, r = .90. Those people who attended every class tended to get 90 points and higher, those who attended most classes usually scored about 80-89 points, those who attended half of the classes scored between 70 and 79 points, those who came infrequently tend to score between 60 and 69 points, and those who never attended usually scored less than 60."

Regression Intro Question 2: Imagine now that you were asked to predict how another student in the class would perform. If you learned that that student never attended class, what grade/score would you expect for this student?

A (90 - 100 pts)

B (80 - 89 pts)

C (70 - 79 pts)

D (60 - 69 pts)

F (<60 pts)