A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.698 b=37.226 r²=0.799236 r=-0.894 Use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place)

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A regression was run to determine if there is a
relationship between hours of TV watched per
day (x) and number of situps a person can do (y).
The results of the regression were:
y=ax+b
a=-0.698
b=37.226
r2=0.799236
r=-0.894
Use this to predict the number of situps a person
who watches 14 hours of TV can do (to one
decimal place)
Transcribed Image Text:A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y=ax+b a=-0.698 b=37.226 r2=0.799236 r=-0.894 Use this to predict the number of situps a person who watches 14 hours of TV can do (to one decimal place)
Expert Solution
Step 1

A linear regression model corresponds to a linear regression model that minimizes the sum of squared linear regression model errors for a set of pairs. The linear regression equation, also known as the least-squares equation has the following form: Y^, where the regression coefficients a and b are computed. 

Given Information : 

A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y) . The results of the regression were provided .  

Linear regssion line equation : Y = a + bx 

Where , a & b are the regression coefficients . 

Correlation coefficient is given as : r = -0.894 

Coefficient of determination is given as : r2 = 0.79936

 

 

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