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In Professor Friedman's economics course the correlation between the students' total scores prior to the final examination and their final-examination scores is r = 0.5. The pre-exam totals for all students in the course have mean 275 and standard deviation 38. The final-exam scores have mean 75 and standard deviation 6. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 290. He decides to predict her final-exam score from her pre-exam total. (a) What is the slope of the least-squares regression line of final-exam scores on pre-exam total scores in this course? (Round your answer to four decimal places.) What is the intercept? (Round your answer to two decimal places.) (b) Use the regression line to predict Julie's final-exam score. (Round your answer to one decimal place.) (c) Julie doesn't think this method accurately predicts how well she did on the final exam. Use r2 to argue that her actual score could have been much higher (or much lower) than the predicted value. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be

In Professor Friedman's economics course the correlation between the students' total scores prior to the final examination and their final-examination scores is r = 0.5. The pre-exam totals for all students in the course have mean 275 and standard deviation 38. The final-exam scores have mean 75 and standard deviation 6. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 290. He decides to predict her final-exam score from her pre-exam total. (a) What is the slope of the least-squares regression line of final-exam scores on pre-exam total scores in this course? (Round your answer to four decimal places.) What is the intercept? (Round your answer to two decimal places.) (b) Use the regression line to predict Julie's final-exam score. (Round your answer to one decimal place.) (c) Julie doesn't think this method accurately predicts how well she did on the final exam. Use r2 to argue that her actual score could have been much higher (or much lower) than the predicted value. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be

MATLAB: An Introduction with Applications
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
Section: Chapter Questions
Problem 1P
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In Professor Friedman's economics course the correlation between the students' total scores prior to the final examination and their final-examination scores is r = 0.5. The pre-exam totals for all students in the course have mean 275 and standard deviation 38. The final-exam scores have mean 75 and standard deviation 6. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 290. He decides to predict her final-exam score from her pre-exam total. (a) What is the slope of the least-squares regression line of final-exam scores on pre-exam total scores in this course? (Round your answer to four decimal places.) What is the intercept? (Round your answer to two decimal places.) (b) Use the regression line to predict Julie's final-exam score. (Round your answer to one decimal place.) -2 (c) Julie doesn't think this method accurately predicts how well she did on the final exam. Use r² to argue that her actual score could have been much higher (or much lower) than the predicted value. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score.
Transcribed Image Text:In Professor Friedman's economics course the correlation between the students' total scores prior to the final examination and their final-examination scores is r = 0.5. The pre-exam totals for all students in the course have mean 275 and standard deviation 38. The final-exam scores have mean 75 and standard deviation 6. Professor Friedman has lost Julie's final exam but knows that her total before the exam was 290. He decides to predict her final-exam score from her pre-exam total. (a) What is the slope of the least-squares regression line of final-exam scores on pre-exam total scores in this course? (Round your answer to four decimal places.) What is the intercept? (Round your answer to two decimal places.) (b) Use the regression line to predict Julie's final-exam score. (Round your answer to one decimal place.) -2 (c) Julie doesn't think this method accurately predicts how well she did on the final exam. Use r² to argue that her actual score could have been much higher (or much lower) than the predicted value. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score. O Only 25% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ could be quite different from the real score. O Exactly 50% of the variability in final exam scores is accounted for by the regression, so the estimate of ŷ should be very close to the real score.
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