Question: The data in the following table represents the Entrance Scores (X) of students who are just beginning to learn about statistics, and their Exit Scores (Y) when they complete the statistics course. Build a linear regression equation to represent this data. We want an equation to use for future use in predicting the Exit scores from knowledge of a new student's Entrance Scores. the maximum possible score on each test is 100. Derive your equation. If you want to check if your equation is correct or not, compare it to what others get it in the discussion forum.

To determine someone's grade, once the equation is derived, all we have to do is to give a person an entrance examination and use that score in our derived equation. This predicts their final grade in the course. If someone's final grade is predicted as being in the range of 90-100 then his/her final grade would be an A. Accordingly, the final grade would be a B for a range of 80-89, and so forth. Therefore, for any course, all we have to do is to build a predictor equation of final grades, give a registered student the entrance examination and issue the grade. The student would not have to spend any time in class and all could be on vacation. Wouldn't this be a good method to reduce class size?

Submit your answer in a Word document with a statement of the problem, your equation and your conclusion as to whether it is a valid equation to use. (We know it isn't because of the small number of student scores we use, but we will forget about that problem for now). You should always use a large number of subjects when building a predictor equation.