In Professor Krugman’s economics course, the correlation between the students’ total scores prior to the final examination and their final‑examination scores is ?=0.5. The pre‑exam totals for all students in the course have mean 280280 and standard deviation 40. The final‑exam scores have mean 75 and standard deviation 8. Professor Krugman has lost Julie’s 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? ?̂ =−47+0.1?y^=−47+0.1x ?̂ =47+0.1?y^=47+0.1x ?̂ =47−0.1?y^=47−0.1x ?̂ =−0.1−47?y^=−0.1−47x Which of the choices is correct interpretation of the slope in the context of the problem? For each point of a student's pre‑exam score means an drop of 0.10.1 point on the final exam, on average. For each point of a student's pre‑exam score means an additional 47 points on the final exam, on average. For each point of a student's pre‑exam score means an additional 0.1 point on the final exam, on average. For each point of a student's pre‑exam score means an drop of 47 points on the final exam, on average. (b) Use a regression line to predict Julie’s 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 ?2 to argue that her actual score could have been much higher (or much lower) than the predicted value.

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Description: In Professor Krugman’s economics course, the correlation between the students’ total scores prior to the final examination and their final‑examination scores is ?=0.5. The pre‑exam totals for all students in the course have mean 280280 and standard d

MATLAB: An Introduction with Applications

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In Professor Krugman’s economics course, the correlation between the students’ total scores prior to the final examination and their final‑examination scores is ?=0.5. The pre‑exam totals for all students in the course have mean 280280 and standard deviation 40. The final‑exam scores have mean 75 and standard deviation 8. Professor Krugman has lost Julie’s 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?

?̂ =−47+0.1?y^=−47+0.1x

?̂ =47+0.1?y^=47+0.1x

?̂ =47−0.1?y^=47−0.1x

?̂ =−0.1−47?y^=−0.1−47x

Which of the choices is correct interpretation of the slope in the context of the problem?

For each point of a student's pre‑exam score means an drop of 0.10.1 point on the final exam, on average.

For each point of a student's pre‑exam score means an additional 47 points on the final exam, on average.

For each point of a student's pre‑exam score means an additional 0.1 point on the final exam, on average.

For each point of a student's pre‑exam score means an drop of 47 points on the final exam, on average.

(b) Use a regression line to predict Julie’s 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 ?2 to argue that her actual score could have been much higher (or much lower) than the predicted value.

Since ?2=0.25r is not exactly 0, then the regression line does predict final scores very well. Julie could have a much higher or lower score.

Since ?2=0.25 is positive, then the regression line does not predict final scores very well. Julie could have a much higher score.

Since ?2=0.5 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 ?2=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.

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