USGross Budget Run Time ($M) Movie ($M) (minutes) Stars White Noise 56.094360 30 101 2 Coach Carter 67.264877 45 136 3 Elektra 24.409722 65 100 2 Racing Stripes 49.772522 30 110 3 Assault on Precinct 13 20.040895 30 109 3 Are We There Yet? 82.674398 20 94 2 Alone in the Dark 5.178569 20 96 1.5 Indigo 51.100486 25 105 3.5 We want a regression model to predict USGross. Parts of the regression output computed in Excel look like this: Dependent variable is USGross($) R-squared = 47.4% R-squared (adjusted) = 46.0% s = 46.41 with 120 – 4 = 116 degrees of freedom Variable Coefficient SE(Coeff) t-Ratio P-Value 25.70 -0.895 Intercept Budget($) -22.9898 0.3729 1.13442 0.1297 8.75 <0.0001 Stars 24.9724 5.884 4.24 <0.0001 Run Time -0.403296 0.2513 -1.60 0.1113

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Movie profit What can predict how much a motion picture
will make? We have data on a number of movies that includes
the USGross (in $), the Budget ($), the Run Time (minutes),
and the average number of Stars awarded by reviewers. The
first several entries in the data table look like this:
We want a regression model to predict USGross. Parts of
the regression output computed in Excel look like this:
Dependent variable is USGross($)
R-squared = 47.4, R-squared (adjusted) = 46.0,
s = 46.41 with 120 - 4 = 116 degrees of freedom
Variable Coefficient SE(Coeff) t-Ratio P-Value
Intercept -22.9898 25.70 -0.895 0.3729
Budget($) 1.13442 0.1297 8.75 ...0.0001
Stars 24.9724 5.884 4.24 ...0.0001
Run Time -0.403296 0.2513 -1.60 0.1113
a) Write the multiple regression equation.
b) What is the interpretation of the coefficient of Budget
in this regression model?
USGross Budget Run Time
($M)
Movie
($M) (minutes) Stars
White Noise
56.094360
30
101
2
Coach Carter
67.264877
45
136
3
Elektra
24.409722
65
100
2
Racing Stripes
49.772522
30
110
3
Assault on Precinct 13 20.040895
30
109
3
Are We There Yet?
82.674398
20
94
2
Alone in the Dark
5.178569
20
96
1.5
Indigo
51.100486
25
105
3.5
We want a regression model to predict USGross. Parts of
the regression output computed in Excel look like this:
Dependent variable is USGross($)
R-squared = 47.4% R-squared (adjusted) = 46.0%
s = 46.41 with 120 – 4 = 116 degrees of freedom
Variable
Coefficient SE(Coeff) t-Ratio P-Value
25.70
-0.895
Intercept
Budget($)
-22.9898
0.3729
1.13442
0.1297
8.75
<0.0001
Stars
24.9724
5.884
4.24
<0.0001
Run Time
-0.403296
0.2513
-1.60
0.1113
Transcribed Image Text:USGross Budget Run Time ($M) Movie ($M) (minutes) Stars White Noise 56.094360 30 101 2 Coach Carter 67.264877 45 136 3 Elektra 24.409722 65 100 2 Racing Stripes 49.772522 30 110 3 Assault on Precinct 13 20.040895 30 109 3 Are We There Yet? 82.674398 20 94 2 Alone in the Dark 5.178569 20 96 1.5 Indigo 51.100486 25 105 3.5 We want a regression model to predict USGross. Parts of the regression output computed in Excel look like this: Dependent variable is USGross($) R-squared = 47.4% R-squared (adjusted) = 46.0% s = 46.41 with 120 – 4 = 116 degrees of freedom Variable Coefficient SE(Coeff) t-Ratio P-Value 25.70 -0.895 Intercept Budget($) -22.9898 0.3729 1.13442 0.1297 8.75 <0.0001 Stars 24.9724 5.884 4.24 <0.0001 Run Time -0.403296 0.2513 -1.60 0.1113
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