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=0.657+1.3665Xi.  Complete parts​ (a) through​ (d)

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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=0.657+1.3665Xi.  Complete parts​ (a) through​ (d).

a. Determine the coefficient of determination, r2, and interpret its meaning.
r2 =
(Round to three decimal places as needed.)
Interpret the meaning of r2.
The value of r2 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 is 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 in 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, r2, and interpret its meaning. r2 = (Round to three decimal places as needed.) Interpret the meaning of r2. The value of r2 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 is 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 in 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
Opening Weekend
Box Office Gross
Online Trailer
Online Trailer
Views (millions)
60.653
Views (millions)
(Smillions)
32.068
(Smillions)
6.543
64.808
19.969
7.481
8.120
4.324
33.038
7.209
3.685
3.637
7.958
27.688
42.520
90.907
86.186
101.842
4.989
4.690
33.552
65.773
6.630
33.377
20.207
15.171
0.942
3.705
6.834
11.819
2.258
1.513
1.817
11.117
11.327
18.470
8.966
15.177
45.832
37.548
12.202
5.273
21.938
4.357
28.916
14.217
13.714
30.436
6.778
5.886
31.231
53.003
46.607
52.612
16.235
6.884
59.248
143.376
7.132
12.652
13.003
10.575
11.650
3.776
11.894
5.181
7.206
1.716
0.897
11.698
18.223
0.895
2.827
3.471
1.789
23.075
13.602
5.315
7.078
12.606
40.011
4.221
0.826
1.385
4.742
2.554
27.536
20.130
3.404
1.207
28.236
95.358
7.273
2.300
7.926
13.089
3.323
3.303
4.267
10.951
6.788
8.344
11.614
13.501
9.862
3.790
50.650
53.315
7.597
2.241
6.007
12.912
31.477
21.812
8.489
7.067
5.106
7.264
5.020
1.985
11.061
12.748
7.739
22.800
61.002
43.603
16.795
13.689
77.545
173.184
7.643
2.080
Transcribed Image Text:Opening Weekend Box Office Gross Opening Weekend Box Office Gross Online Trailer Online Trailer Views (millions) 60.653 Views (millions) (Smillions) 32.068 (Smillions) 6.543 64.808 19.969 7.481 8.120 4.324 33.038 7.209 3.685 3.637 7.958 27.688 42.520 90.907 86.186 101.842 4.989 4.690 33.552 65.773 6.630 33.377 20.207 15.171 0.942 3.705 6.834 11.819 2.258 1.513 1.817 11.117 11.327 18.470 8.966 15.177 45.832 37.548 12.202 5.273 21.938 4.357 28.916 14.217 13.714 30.436 6.778 5.886 31.231 53.003 46.607 52.612 16.235 6.884 59.248 143.376 7.132 12.652 13.003 10.575 11.650 3.776 11.894 5.181 7.206 1.716 0.897 11.698 18.223 0.895 2.827 3.471 1.789 23.075 13.602 5.315 7.078 12.606 40.011 4.221 0.826 1.385 4.742 2.554 27.536 20.130 3.404 1.207 28.236 95.358 7.273 2.300 7.926 13.089 3.323 3.303 4.267 10.951 6.788 8.344 11.614 13.501 9.862 3.790 50.650 53.315 7.597 2.241 6.007 12.912 31.477 21.812 8.489 7.067 5.106 7.264 5.020 1.985 11.061 12.748 7.739 22.800 61.002 43.603 16.795 13.689 77.545 173.184 7.643 2.080
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