Question

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.

Answer 1