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In Professor Krugmans 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 280 and standard deviation 40. The final-exam scores have mean 75 and standard deviation 8. Professor Krugman has lost Julies final exam but knows that her total before the exam was 300. He decides to predict her final-exam score from her pre-exam total (a) Which of the choices is the least-squares regression line of final-exam scores on pre-exam total scores in this course? О У-47 + 0.1 x Oy=47-0.1x 47+0.1x Which of the choices is correct interpretation of the slope in the context of the problem? O For each point of a students pre-exam score means an drop of 0.1 point on the final exam, on average OFor each point of a students pre-exam score means an drop of 47 points on the final exam, on average For each point of a students pre-exam score means an additional 0.1 point on the final exam, on average For each point of a students pre-exam score means an additional 47 points on the final exam, on average (b) Use a regression line to predict Julies final-exam score. Give your answer as a whole number Predicted final-score- (c) Julie does not think this method accurately predicts how well she did on the final exam. Select the statement that correctly uses to argue that her actual score could have been much higher (or much lower) than the predicted value Since 2 0.25 is not exactly Q then the regression line does predict final scores very well. Julie could have a much higher or lower score Since r2-0.25 is close to 0, then the regression line does not predict final scores very well. Julie could have a much higher or lower score Since r2-0.5 is close to 0, then the regression line does not predict tinal scores very well. Julie could have a much higher or lower score Since0.25 is positive, then the regression line does not predict final scores very well. Julie could have a much higher score

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