Consider the following combinations of average test score and the average student-teacher ratio in some fictional school districts. 19, 20, 22 23.5. 25 670, 660, 630, 660. 635 Student-teacher ratio (X): 15, 17, Test-score (Y): 680, 640, Estimate the linear regression line. y=-713.57+2.9x y=-2.97+713.57X Oy=2.97+713.57x O y=713.57-2.97x

MATLAB: An Introduction with Applications
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Author:Amos Gilat
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Chapter1: Starting With Matlab
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Consider the following combinations of average test score and the average student-teacher ratio in some fictional school districts.
Student-teacher ratio (X): 15. 17, 19, 20, 22. 23.5. 25
680, 640, 670, 660. 630, 660, 635
Test-score (Y):
Estimate the linear regression line.
Oy=-713.57+2.9x
O y=-2.97+713.57X
Oy=2.97+713.57x
O y=713.57-2.97x
QUESTION 2
(Continued) Test the hypothesis HB₁=0 vs. H_ :B₁ #0
1
O T=-1.51, P-value=0.38
O T=-1.51, P-value=0.19
O T=1.51, P-value=0.19
O T=1.51, P-value=0.38
QUESTION 3
(Continued) Compute R² and interpret it.
0.31: About 31% of variation in test score is explained by variation in student-teacher ratio through the model.
0.31: About 31% of variation in student-teacher ratio is explained by variation in test score through the model.
O 0.69: About 69% of variation in test score is explained by variation in student-teacher ratio through the model.
O 0.69: About 89% of variation in student-teacher ratio is explained by variation in test score through the model.
QUESTION 4
(Continued) Find the mean test score when the student-teacher ratio is 18.
645.23
654.12
O 660.11
O 523.61
QUESTION 5
(Continued) Find the fitted value of y corresponding to x=20 and the associated residual
O 660.1; 0
O 645.16; 14.84
O654.16; 5.84
O 660.1: -5.84
QUESTION 6
(Continued) Find the fitted value of y corresponding to x=20 and the associated residual
660.11; 0.11
O 645.16: 14.84
O654.16; 5.84
O 660.1; -5.84
Transcribed Image Text:Consider the following combinations of average test score and the average student-teacher ratio in some fictional school districts. Student-teacher ratio (X): 15. 17, 19, 20, 22. 23.5. 25 680, 640, 670, 660. 630, 660, 635 Test-score (Y): Estimate the linear regression line. Oy=-713.57+2.9x O y=-2.97+713.57X Oy=2.97+713.57x O y=713.57-2.97x QUESTION 2 (Continued) Test the hypothesis HB₁=0 vs. H_ :B₁ #0 1 O T=-1.51, P-value=0.38 O T=-1.51, P-value=0.19 O T=1.51, P-value=0.19 O T=1.51, P-value=0.38 QUESTION 3 (Continued) Compute R² and interpret it. 0.31: About 31% of variation in test score is explained by variation in student-teacher ratio through the model. 0.31: About 31% of variation in student-teacher ratio is explained by variation in test score through the model. O 0.69: About 69% of variation in test score is explained by variation in student-teacher ratio through the model. O 0.69: About 89% of variation in student-teacher ratio is explained by variation in test score through the model. QUESTION 4 (Continued) Find the mean test score when the student-teacher ratio is 18. 645.23 654.12 O 660.11 O 523.61 QUESTION 5 (Continued) Find the fitted value of y corresponding to x=20 and the associated residual O 660.1; 0 O 645.16; 14.84 O654.16; 5.84 O 660.1: -5.84 QUESTION 6 (Continued) Find the fitted value of y corresponding to x=20 and the associated residual 660.11; 0.11 O 645.16: 14.84 O654.16; 5.84 O 660.1; -5.84
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