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 this data, and the result is the linear regression equation Y; = - 1.248 + 1.4429X. Complete parts (a) through (c) below. Click on the icon to view the data table. a. Determine the coefficient of determination, r2, and interpret its meaning. 2 =(Round to three decimal places as needed.) Interpret the meaning of 2. 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 movie weekend box office gross? O A. It is very useful for predicting box office gross because the coefficient of determination is very close to 1. O B. It is not useful for predicting box office gross because the coefficient of determination is close to 1. O C. It is somewhat useful for predicting box office gross because the coefficient of determination is closer to 1 than it is to 0. O D. It is not useful for predicting box office gross because the coefficient of determination is close to 0.Opening Weekend Opening Weekend Online Trailer Box Office Gross Online Trailer Box Office Gross Views (millions) ($millions) Views (millions) ($millions) 55.142 34.446 3.013 0.396 10.938 6.643 35.148 61.113 11.055 7.343 0.218 14.919 10.205 26.575 41.507 89.481 81.700 103.286 4.989 4.690 33.193 63.107 6.630 33.377 24.280 15.758 0.942 3.705 7.246 13.739 2.258 1.513 2.022 8.916 11.327 18.470 48.274 38.867 8.966 12.202 7.336 15.635 15.177 4.357 29.424 17.104 13.714 30.436 5.548 5.071 31.231 53.003 59.724 149.990 52.612 46.607 10.122 14.323 16.235 13.003 12.986 8.795 6.884 3.776 11.659 0.741 11.698 18.223 1.898 1.216 2.827 3.471 0.498 0.562 23.075 13.602 4.834 7.612 12.606 40.011 4.576 2.754 0.826 1.385 9.053 5.410 27.536 20.130 31.837 99.902 7.273 3.404 0.249 2.074 3.323 1.207 1.815 13.148 4.267 10.951 9.932 12.351 3.790 8.344 52.804 53.607 7.597 11.614 6.252 4.173 12.912 13.501 30.682 20.072 7.067 5.106 6.539 8.090 5.020 1.985 15.762 14.544 7.739 22.800 56.310 37.075 16.795 13.689 78.648 174.882 7.643 2.080