OLS Regression Results Dep. Variable: Metascore R-squared: 0. 167 Model: OLS Adj. R-squared: 0. 166 Method: Least Squares F-statistic: 279. 4 Date: Sat, 22 Oct 2022 Prob (F-statistic): 0.00 Time: 09: 31:26 Log-Likelihood: -46535. No. Observations: 9779 AIC: 9. 309e+04 Df Residuals: 9771 BIC: 9. 314e+04 Df Model: Covariance Type: nonrobust coef std err t P> | t | [0 . 025 0.975] intercept 29. 4352 2. 634 11. 176 0. 000 24.272 34. 598 Rating 1. 7575 0. 361 4. 863 0.000 1. 049 2. 466 Runtime -0. 0901 0. 014 -6. 224 0.000 -0. 118 -0. 062 Gross 5. 866e-08 7.72e-09 7. 600 0.000 4.35e-08 7.38e-08 Votes 8. 69e-05 3.93e-06 22. 097 0.000 7.92e-05 9. 46e-05 Action -11. 1038 0.743 -14.952 0.000 -12. 559 -9. 648 Comedy -3. 9175 0. 632 -6. 197 0.000 -5. 157 -2. 678 Horror -10. 8594 0. 956 -11. 356 0.000 -12. 734 -8. 985 Omnibus : 9953. 113 Durbin-Watson: 1. 821 Prob (Omnibus ) : 0. 000 Jarque-Bera (JB) : 670. 465 Skew: 0. 205 Prob ( JB) : 2.57e-146 Kurtosis: 1. 784 Cond. No. 5.38e+08 Notes : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 5.38e+08. This might indicate that there are strong multicollinearity or other numerical problems.. What's the equation of the model? . which explanatory variables have statistically significant correlation with Metascore? . Use the model to predict the Metascore of a movie that has 6.53 rating, 100 minutes long, made $3 million, received 14000 votes, and belongs to Comedyr genre. . Use the model to predict the Metascore of a movie that has the same profile as above except that it belongs to Horror genre instead of Comedy