In chapter 12, we found a statistically significant correlation between overall performance in class and how much time someone studied. Use the summary statistics calculated in that problem (provided here) to compute a line of best fit predicting success from study times: X = 1.61, sx = 1.12, Y = 2.95, sy = 0.99, r = 0.65. Using the line of best fit equation created in problem 7, predict the scores for how successful people will be based on how much they study: а. Х%3D 1.20 b. X= 3.33 с. X%3D 0.71 d. X= 4.00

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7. In chapter 12, we found a statistically significant correlation between overall
performance in class and how much time someone studied. Use the
summary statistics calculated in that problem (provided here) to compute a
line of best fit predicting success from study times: X = 1.61, sx =1.12, Y =
2.95, sy = 0.99, r = 0.65.
8. Using the line of best fit equation created in problem 7, predict the scores for
how successful people will be based on how much they study:
а. Х3D 1.20
%3D
b. X= 3.33
с. Х%3D0.71
d. X= 4.00
Transcribed Image Text:7. In chapter 12, we found a statistically significant correlation between overall performance in class and how much time someone studied. Use the summary statistics calculated in that problem (provided here) to compute a line of best fit predicting success from study times: X = 1.61, sx =1.12, Y = 2.95, sy = 0.99, r = 0.65. 8. Using the line of best fit equation created in problem 7, predict the scores for how successful people will be based on how much they study: а. Х3D 1.20 %3D b. X= 3.33 с. Х%3D0.71 d. X= 4.00
variance.
6. Least Squares Error Solution; the line that minimizes the total amount of
residual error in the dataset.
7. b= r*(s,/Sx) = 0.65*(0.99/1.12) = 0.72; a = Y – bX = 2.95 – (0.72*1.61) =
1.79; Y = 1.79 + 0.72X
Transcribed Image Text:variance. 6. Least Squares Error Solution; the line that minimizes the total amount of residual error in the dataset. 7. b= r*(s,/Sx) = 0.65*(0.99/1.12) = 0.72; a = Y – bX = 2.95 – (0.72*1.61) = 1.79; Y = 1.79 + 0.72X
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