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
According to the information from the exercise, we can deduce that the regression technique is the method of analysis for IMDb ratings and Rotten tomato ratings, so we have a dependent variable such as rotten tomatoes and the independent one that is IMDb-rating. We can say that in this analysis that the variation of the dependent variable is explained by the coefficient of determination, we observe that the coefficient of determination is 0.63 if we convert it into a percentage we obtain that it is equal to 63.65% and we approach 64%, with which which we affirm that it is equal to 64, so this value is the influence that the IMDb ratings generated on the ratings of rotten tomatoes
