Download the Case Study Data (CSV) file required for this assignment. A Answer the questions presented within the Case Study. To complete your analysis of the case, you will need to apply all the tools and techniques you have learned in this course. Note: Access the attached CSV data file to complete this assignment. Submission Instructions . When you have finished the assignment and reviewed your answers, save your work. . Then follow the instructions in Blackboard to submit your completed Word document.Dr. Scott Dressler V B L: S I N E S S Instructions: - Transfer this entire document to a MS Word document. I On this html document, press I"Citrl + a\" (select all}, then press I'Gtri + cII (copy all). 0 Open a new MS Word document, press \"Ctrl + \\f' {paste all}. . If the formatting looks the same, you're done. If not, press "G'trl' alone {to open the paste menu) and then press \"k\" (keep source formatting). I Answer each question as briefly and completely as possible on a Microsoft Word document. Be sure to include the R commands along with your answers. Scenario: Why do some movies make more money at the box office than others? Hollywood is a mu lti-billion dollar industry which releases more than a hundred lms a year, with large variations in the budgets and box office grosses ofthe movies. Identifying which factors are important to a movie's profitability and subsequently predicting the success of a movie given its relevant parameters could save movie studios hundreds of millions of dollars a year. Using a dataset containing information on 500 movies, your job is to construct a multiple regression to determine the characteristics of a movie that help predict success at the box office. The variables are as follows: YE: Movie is box office revenue in millions of U.S. dollars (USU). This is only the domestic revenue received at US. theaters during the first min of the movie {i.e., no DVD sales, international revenue, etc.}. The variable is in inflation acy'usted terms so we can compare movie revenue witout having to worry about changes in the price level. X\": Production Budget of Movier' in millions of USD. XX: Critical review of Movie r' from Flotten Tomatoes (range: 0-100}. X\": Number of official trailer views on YouTube in millions of views. X\": Star Power of Movie i. This is a relative measure that determines the overall caliber of movie stars in the cast. A high number for Star Power means that Movie 1' has a cast containing a high number of famous actors. The average of this variable is 100, so if a movie has a star power rating of 120, then that means it is 20% above average. Ky: Established Audience of Movie 1'. This is a dummy variable that equals 1 if Movie I' is based off of a book or is a sequel to another movie tzero otherwise). X5: ls Movie I' a Chifd'rens' Movie? This is a dummy variable that equals 1 if Movie I' is considered a childrens' movie {zero othenrvise}. 1. (10 pm) Test your independent variables (X1,- through Xsrl for potential multicollinearity. Fleport the variance inflation factor (\\i'lF) for each independent variable, and state your conclusion. If your test suggests that you should remove an independent variable from your analysis, then remove it and test the remaining independent variables for mufticollinearity again. This process is to be continued until you can show evidence that all remaining independent variables pass a multicollinearity test. 2. (10 pm) Run a regression with box ofce revenue {YJ as your dependent variable and the remaining X variables as your independent variables. Check if any ofthe regression coefficients are insignificantly different from zero. If so, remove the corresponding independent variables (one at a time} from your analysis and arrive at a model where all regression coefcients are statistically significant. Only state your final regression results. 3. (10 pm) Using your results from question 2, perform a partial residual analysis bytesting the linearity assumption of your regression. Does there appear to be a pattern in any of the relationships between the residuals and your independent variables? If so, then attempt to resolve the issue by adding a squared -term (i.e.r a quadratic transformation). State your final regression resuhs which successfully pass a residual analysis. 4. (15 pm) Using your results from question 3, answer the following questions. a. What is the R2 value? What does this say in the context of the scenario? b. Interpret each of the relationships between box office revenue and the independent variables. In the occurrence of a linear and quadratic term, be sure to interpret both terms iointly and not separately. c. Test the following hypothesis: For every $1 increase in production budget on average. a movie will increase box office revenue by MORE THAN $1 . Be sure to state the null and alternative hypotheses, the p-vaiue of the test, and your conclusion. Note: your conclusion should be made with 95% confidence or more