Question

# Many movies are released each year and it would be interesting to be able to predict...

Many movies are released each year and it would be interesting to be able to predict the Total Gross Revenues (in \$1,000,000) from the box office based on a few predictors. The following predictors have been identified for 70 movies:

• BUDGET: Estimated budget in \$1,000,000
• LENGTH: The length of each movie in minutes
• SCREENS: Number of Screens on Opening Weekend
• AWARDS: Number of Award nominations of entire cast in their careers
• GENRE: Type of movie: Action, Comedy or Drama recoded as Genre 1 and Genre 2
• Genre1 = 1 if Action, 0 if not
• Genre2 = 1 if Comedy, 0 if not

The following partial output has been obtained:

 Coefficients Standard Error t Stat Intercept -4.039 30.735 Budget 0.803 0.154 Length -0.433 0.242 Screens 0.013 0.005 Awards 1.390 1.049 Genre 1 4.777 2.032 Genre 2 2.732 13.646
 ANOVA df SS MS F Regression 39.5 Residual 170.2 Total

Based on the above partial printout, answer the following questions:

1. What is the regression model?
2. Interpret the following coefficients: -0.433 and 4.777.
3. Is there sufficient evidence at the 5% level of significance to conclude that the model is useful at predicting Total Gross Revenues?
4. Determine the adjusted coefficient of determination and explain its meaning in the context of the problem.
5. What is the standard error of the estimate?
6. Does the “genre” of movie have a significant impact (at 5%) on the Total Gross Revenues? Justify.
7. Estimate the Total Gross Revenues for a Drama movie of 90 minutes produced with a \$25,000,000 budget with a cast of actors who were nominated 6 times for awards. In addition, that movie was shown on 2,000 screens in the first weekend.