You have a sample of data on 1554 movies released in the United States between 1978 and 2013. You have information on the following variables: The total gross revenue of the movie made as of May 22, 2013 in millions of Lifetime Gross 2011 dollars Year The year of Date of Release Month Month in which movie was released. The estimated total cost of producing the movie, in millions of 2011 US Budget Dollars Mpaarating Letter s assigned " G, PG, PG-13 and R You created indicator/dummy variables for mpaarating and season and then estimated 3 regression equations. Please refer to the summary outputs for the 3 separate regressions at the end of this document. 7. Interpret the R2 of Regression 1. 8. Interpret the coefficient on budget for Regression 1. 9. Interpret the coefficient on PG in Regression 1. 10. Interpret the coefficient on spring in Regression2. What do we learn from the corresponding t stat or p- value? 11. From Regression 3 with 95% confidence is there a difference in lifetime gross on average between summer and winter, holding mearatings and budget constant? Explain/Show work. 12. Which Regression would you present to the Hollywood executive and why? 13. The movie Avatar had a lifetime gross of $760M and its budget was $237M, It had an mpaarating of PG-13 and was released in Dec. (winter). With 95% confidence would you have expected Avatar to have earned so much? In other words, was the movie exceptionally successful? Explain and show all work.Regression 1 SUMMARY OUTPUT Regression Statistics Multiple R 0.626845 R Square 0.392935 Adjusted R Square 0.391367 Standard Error 59.68296 Observations 1554 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 9.280397 2.664302 3.483237 0.000509 4.054377 14.50642 budget ($mil) 1.027376 0.035666 28.80586 1.8E-146 0.957418 1.097333 G 33.59386 8.584782 3.913187 9.58-05 16.75483 50.43288 PG 13.72358 4.62602 2.966606 0.003057 4.649657 22.7975 PG-13 12.32592 3.479118 3.542828 0.000408 5.50164 19.1502 Regression 2 SUMMARY OUTPUT Regression Statistics Multiple R 0.623289 R Square 0.388489 Adjusted R Square 0.38691 Standard Error 59.90107 Observations 1554 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 18.44373 3.538592 5.212168 2.12E-07 11.5028 25.38467 budget ($mil) 1.056984 0.03466 30.49611 2.2E-160 0.988999 1.124968 Spring -2.16268 4.442572 -0.48681 0.626463 -10.8768 6.551411 Summer 4.301554 4.384933 0.980985 0.326753 -4.29948 12.90259 Fall -10.6677 4.372027 -2.43998 0.0148 -19.2434 -2.09193Regression 3 SUMMARY OUTPUT Multiple R 0.630434 R Square 0.397447 Adjusted R Square 0.394719 Standard Error 59.5184 Observations 1554 Standard Upper Coefficients Error t Stat P-value Lower 95% 95% Intercept 12.53983 3.802416 3.29786 0.000996 5.081396 19.99827 budget ($mil) 1.017495 0.035794 28.42613 2.4E-143 0.947285 1.087706 G 32.53874 8.572433 3.795742 0.000153 15.72392 49.35357 PG 13.38964 4.618734 2.898984 0.003797 4.329992 22.44928 PG-13 11.61161 3.47755 3.33902 0.000861 4.790398 18.43283 Spring -2.4653 4.418329 -0.55797 0.576944 -11.1319 6.201248 Summer 3.430418 4.361104 0.786594 0.43164 -5.12389 11.98472 Fall -10.3013 4.348091 -2.36916 0.017951 -18.8301 -1.77253