Statistics
posted by Jannel on .
In Professor Smith's statistics course, the correlation between students' total scores before the final exam and their final exam scores is r = 0.67. The preexam totals for all students in the course have a mean of 275 and a standard deviation of 26. The final exam scores have a mean of 71 and a standard deviation of 6. Professor Smith has lost Jane's final exam, so decides to predict her final exam score based on her preexam course total, which is 293. Use leastsquares regression to predict Jane's final exam score.
Predicted Final Exam Score =
Please explain to me how to do this. My teacher does not teach this kind of stuff in class and he expects us to know this.

Preexam: mean = 275, sd = 26
Final exam: mean = 71, sd = 6
Correlation: r = 0.67
Regression equation is in this format:
predicted y = a + bx
...where a = intercept and b = slope.
To find the equation, you need to substitute the information given in the problem into a workable formula:
predicted y = (rSy/Sx)X  (rSy/Sx)xbar + ybar
...where r = correlation, Sy = sd of y, Sx = sd of x, and X is the variable in 'a + bx' equation.
Note: xbar = mean of x; ybar = mean of y.
I'll let you take it from here. (Once you calculate the predicted y formula, substitute 293 for x in the formula to predict Jane's final exam score.)