We analyzed the relationship between the budgets (in millions of dollars) and the U. S. Box Office Sales (in millions of dollars) for 75 popular movies. The scatterplot showed a weak positive association with a linear form. Here are the StatCrunch linear regression results:
Simple linear regression results:
Dependent Variable: US_Box_Office
Independent Variable: Budget US_Box_Office
IMDb_Rating Rotten Tomatoes = -57.599288 + 17.919209 IMDb_Rating Sample size: 75
R(correlation coefficient) = 0.79786226
R-sq = 0.63658418
Estimate of error standard deviation: 12.916488
Which number describes the percentage of variability in Rotten Tomato ratings that is explained by the changes in IMDb ratings as described by the regression line? This is the slope of the regression line. It describes the predicted change in Rotten Tomato ratings when IMDb ratings increase by one.
a. 0.80
b. 0.64
c. 12.9
d. 17.9

High school seniors with strong academic records apply to the nation’s most selective colleges in greater numbers each year. because the number of slots remains relatively stable, some colleges reject more early applicants. suppose that for a recent admissions class, an ivy league college received 2851 applications for early admission. of this group, it admitted 1033 students early, rejected 854 outright, and deferred 964 to the regular admission pool for further consideration. in the past, this school has admitted 18% of the deferred early admission applicants during the regular admission process. counting the students admitted early and the students admitted during the regular admission process, the total class size was 2375. let e, r, and d represent the events that a student who applies for early admission is admitted early, rejected outright, or deferred to the regular admissions pool. suppose a student applies for early admission. what is the probability that the student will be admitted for early admission or be deferred and later admitted during the regular admission process?