Moddel Parameters (Points) Source Value Standard Error t Pr > |t| Intercept 0.437798 5.9182 0.073948 0.9428 Hours 0.829539 0.109474 7.57748 0.0001 Analysis of Variance (Points) Source Sum of Squares Df Mean Square F Pr > |t| Model 3249.72 1 3249.72 57.42 0.0001 Error 452.779 8 56.5974 Corrected Total 3702.5 9 1. Use the estimatedd regression to predict the points of a student who spent 50 hours studying for this course. A. Y=50, so Y^=50 B. X=50, so Y^=0.83+0.44(50)=22.83 C. X=50, so Y^=0.44+0.83(50)=41.94 D. Unable to find. 1a. If a student spennt 120 hours studying for this course, would you feeel comfortable to use your estimated regresssion equation to predict his points? Why? A. The range of X in our data is from 10 to 85. X = 120 is outside the range of X. I do not feel comfortable to use my estimated regression equation to predict his points as the linear relationship may not hold when X = 120. B. I know that 120 hours is twice of 60 hours. Student No. 8 spent 60 hours and earned 58 points. A student who spent 120 hours will probably earn 58 x 2 = 116 points. C. I would substitute X = 120 into my estimated regression equation to calculate his points. 1b. What's the R-squared? A. 452.779 / 3702.5 = 0.12 B.(3249.72/ 3702.5)^2 = 0.77 C. 3702.5 / 3249.72 = 1.14 D. 3249.72/ 3702.5 = 0.88 1c. Do you believe your estimated regression equation would provide a good prediction of the points? Use R-squared from Question 8 to support your answer. A. No. R-squared is less than 70%. Our estimated regression equation does not provide a good prediction for all the points. B. Yes. R-squared is more than 70%. Our estimated regression equation provides a good prediction for all the points. C. Yes. R-squared is less than 70%. Our estimated regression equation provides a good prediction for all the points. D. No. R-squared is more than 70%. Our estimated regression equation does not provide a good prediction for all the points.

MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
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Moddel Parameters (Points)

Source Value Standard Error         t Pr > |t|
Intercept 0.437798 5.9182 0.073948 0.9428
Hours 0.829539 0.109474 7.57748 0.0001

Analysis of Variance (Points)

Source Sum of Squares Df Mean Square F Pr > |t|
Model 3249.72 1 3249.72 57.42 0.0001
Error 452.779 8 56.5974    
Corrected Total 3702.5 9    

1. Use the estimatedd regression to predict the points of a student who spent 50 hours studying for this course.

A. Y=50, so Y^=50
B. X=50, so Y^=0.83+0.44(50)=22.83
C. X=50, so Y^=0.44+0.83(50)=41.94
D. Unable to find.
1a. If a student spennt 120 hours studying for this course, would you feeel comfortable to use your estimated regresssion equation to predict his points? Why?

A. The range of X in our data is from 10 to 85. X = 120 is outside the range of X. I do not feel comfortable to use my estimated regression equation to predict his points as the linear relationship may not hold when X = 120.
B. I know that 120 hours is twice of 60 hours. Student No. 8 spent 60 hours and earned 58 points. A student who spent 120 hours will probably earn 58 x 2 = 116 points.
C. I would substitute X = 120 into my estimated regression equation to calculate his points.
1b. What's the R-squared?
A. 452.779 / 3702.5 = 0.12
B.(3249.72/ 3702.5)^2 = 0.77
C. 3702.5 / 3249.72 = 1.14
D. 3249.72/ 3702.5 = 0.88
1c. Do you believe your estimated regression equation would provide a good prediction of the points? Use R-squared from Question 8 to support your answer. 

A. No. R-squared is less than 70%. Our estimated regression equation does not provide a good prediction for all the points.
B. Yes. R-squared is more than 70%. Our estimated regression equation provides a good prediction for all the points.
C. Yes. R-squared is less than 70%. Our estimated regression equation provides a good prediction for all the points.
D. No. R-squared is more than 70%. Our estimated regression equation does not provide a good prediction for all the points.
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