12. A professor has collected data on how the final grade y (number of points out of 100) students get depends on the average number of hours x the student spent each week working on mathematics. She then fit a regression line to the data, which looked close to a straight line. The equation of the best fitting (regression line) is given by y = 3.69x +29.26. a) Explain what the value of the slope means in this situation. Complete the statement below. For each additional hour per week that a student studies on average, their final grade will by points. b) Explain what the value of the y-intercept means in this situation. If a student spends no time each week studying for math, then they should expect to get a final grade of about c) Use the model to find the predicted value for the final grade when a student spends an average of 11 hours each week studying for math. Round your answer to 1 decimal place.

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12. A professor has collected data on how the final grade y (number of points out of 100) students get
depends on the average number of hours x the student spent each week working on mathematics. She
then fit a regression line to the data, which looked close to a straight line. The equation of the best
fitting (regression line) is given by
y = 3.69x +29.26.
a) Explain what the value of the slope means in this situation. Complete the statement below.
For each additional hour per week that a student studies on average, their final grade will
by
points.
b) Explain what the value of the y-intercept means in this situation.
If a student spends no time each week studying for math, then they should expect to get a final
grade of about
c) Use the model to find the predicted value for the final grade when a student spends an average of
11 hours each week studying for math. Round your answer to 1 decimal place.
d) According to the model, the final grade of a student who spends 13 hours each week on math is
predicted to be 77.23, while the grade of a the student in the data set who spend 13 hours actually
was 74.2.
Compute the percentage error for this data point (round to two decimal places) and state whether
the model over or underpredicts for this data point.
Transcribed Image Text:12. A professor has collected data on how the final grade y (number of points out of 100) students get depends on the average number of hours x the student spent each week working on mathematics. She then fit a regression line to the data, which looked close to a straight line. The equation of the best fitting (regression line) is given by y = 3.69x +29.26. a) Explain what the value of the slope means in this situation. Complete the statement below. For each additional hour per week that a student studies on average, their final grade will by points. b) Explain what the value of the y-intercept means in this situation. If a student spends no time each week studying for math, then they should expect to get a final grade of about c) Use the model to find the predicted value for the final grade when a student spends an average of 11 hours each week studying for math. Round your answer to 1 decimal place. d) According to the model, the final grade of a student who spends 13 hours each week on math is predicted to be 77.23, while the grade of a the student in the data set who spend 13 hours actually was 74.2. Compute the percentage error for this data point (round to two decimal places) and state whether the model over or underpredicts for this data point.
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