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.

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

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Question

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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