A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regre
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
A box office analyst seeks to predict opening weekend box office gross for movies. Toward this goal, the analyst plans to use online trailer views as a predictor. For each of the 66 movies, the number of online trailer views from the release of the trailer through the Saturday before a movie opens and the opening weekend box office gross (in millions of dollars) are collected and stored in the accompanying table. A linear regression was performed on these data, and the result is the linear regression equation Yi=−1.259+1.4483Xi.
![a. Determine the coefficient of determination, r, and interpret its meaning.
(Round to three decimal places as needed.)
Interpret the meaning of r
The value of r indicates that
% of the variation in
can be explained by the variation in
(Round to one decimal place as needed.)
b. Determine the standard error of the estimate.
Syx =
(Round to two decimal places as needed.)
c. How useful do you think this regression model is for predicting opening weekend box office gross?
A. It is not useful for predicting box office gross because the coefficient of determination is close to 1.
B. It is not useful for predicting box office gross because the coefficient of determination is close to 0.
C. It is very useful for predicting box office gross because the coefficient of determination is very close to 1.
D. It is somewhat useful for predicting box office gross because the coefficient of determination
closer to 1 than it is to 0.
d. Can you think of other variables that might explain the variation in opening weekend box office gross? Select all that apply
A. The type of movie might explain the variation in opening weekend box office gross, since some genres are more heavily attended than others.
B. The amount spent on advertising might explain the variation in opening weekend box office gross, because viewers are probably more likely to watch a movie that has been advertised heavily.
C. The timing of the release of the movie might explain the variation
opening weekend box office gross, because a movie released at the same time as multiple other major movies may get crowded out.](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fafc75c11-f74c-4700-aac3-dcc346278078%2F492915a4-a437-44ed-9910-ad121220fa31%2Fzrukbfk_processed.jpeg&w=3840&q=75)
![Opening Weekend
Box Office Gross
(Smillions)
Opening Weekend
Box Office Gross
(Smillions)
Online Trailer
Online Trailer
Views (millions)
Views (millions)
4.872
57.799
32.880
4.593
11.595
7.777
37.663
64.369
9.540
0.225
4.169
14.185
8.210
21.871
41.980
87.598
82.427
105.959
4.989
4.690
36.071
61.525
6.630
33.377
22.011
19.915
0.942
3.705
6.105
12.898
2.258
1.513
6.231
45.702
5.960
11.327
18.470
39.911
8.966
12.202
6.995
23.453
15.177
4.357
26.578
13.083
13.714
30.436
5.952
60.130
5.798
31.231
53.003
149.040
52.612
46.607
5.849
8.534
16.235
13.003
13.032
8.931
6.884
3.776
12.750
3.318
11.698
18.223
0.044
1.791
2.827
3.471
1.378
1.970
23.075
13.602
7.206
5.416
12.606
40.011
5.115
7.939
0.826
1.385
6.474
3.585
96.133
27.536
7.273
20.130
33.018
3.404
0.509
4.870
3.323
1.207
1.248
8.237
4.267
10.951
7.888
17.088
3.790
8.344
51.636
51.164
7.597
11.614
2.193
29.323
3.499
12.912
13.501
19.644
7.067
5.106
5.607
9.693
5.020
1.985
13.055
11.228
7.739
22.800
58.437
43.666
16.795
13.689
80.908
177.433
7.643
2.080](/v2/_next/image?url=https%3A%2F%2Fcontent.bartleby.com%2Fqna-images%2Fquestion%2Fafc75c11-f74c-4700-aac3-dcc346278078%2F492915a4-a437-44ed-9910-ad121220fa31%2F9z7x956_processed.png&w=3840&q=75)
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