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An instructor wants to show the students that there is a linear correlation between the number of hours they spent watching TV during a certain weekend and their scores on a test taken the following Monday. The number of television viewing hours and the test scores for 12 randomly selected students are shown in the table below.   Hours (x) 0 1 2 3 3 5 5 5 6 7 7 10 Score (y) 96 85 82 74 95 68 76 84 58 65 75 50   Correlation coefficient is:  r =      The equation of regression line is:   Score = *(Hours)  +    Coefficient of determination:    of the variability in the    is explained by the variability in the  .

An instructor wants to show the students that there is a linear correlation between the number of hours they spent watching TV during a certain weekend and their scores on a test taken the following Monday. The number of television viewing hours and the test scores for 12 randomly selected students are shown in the table below.   Hours (x) 0 1 2 3 3 5 5 5 6 7 7 10 Score (y) 96 85 82 74 95 68 76 84 58 65 75 50   Correlation coefficient is:  r =      The equation of regression line is:   Score = *(Hours)  +    Coefficient of determination:    of the variability in the    is explained by the variability in the  .

MATLAB: An Introduction with Applications
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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An instructor wants to show the students that there is a linear correlation between the number of hours they spent watching TV during a certain weekend and their scores on a test taken the following Monday. The number of television viewing hours and the test scores for 12 randomly selected students are shown in the table below.

 

Hours (x)

0

1

2

3

3

5

5

5

6

7

7

10

Score (y)

96

85

82

74

95

68

76

84

58

65

75

50

 

  • Correlation coefficient is:  r =   
  •  
  • The equation of regression line is:   Score = *(Hours)  + 
  •  
  • Coefficient of determination:    of the variability in the    is explained by the variability in the  .
  •  

 

 

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