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

Algebra & Trigonometry with Analytic Geometry
13th Edition
ISBN:9781133382119
Author:Swokowski
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Chapter7: Analytic Trigonometry
Section7.6: The Inverse Trigonometric Functions
Problem 94E
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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.
Transcribed Image Text: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.
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
Transcribed Image Text: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
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