business Assignment

1) A movie production company in interested in predicting the first year box office sales of a movie. They think the total production cost, total promotional costs and total book sales affect the first year box office sales. Use the BoxOffice spreadsheet.

Create dummy variables for genre!

a) What are the values of , ? Interpret their meaning.

b) What is the least squares prediction equation?

c) What is the 95% prediction interval for the first year box office of a horror movie with total production cost of 10 million, total promotional costs of 7 million, and total book sales of 6 million?

d) Test the significance of the model.

e) Test the significance of each dummy variable.

2) Using MLB data find the best regression equation that predicts the winning percentages. (Include League as a dummy variable only if dummy variables are covered in class.)

a. Find the best regression equation using stepwise regression. Show all steps. What is the least squares regression equation?

b. Find the best regression equation using all possible regressions method. Show your work. What is the least squares regression equation?

Answer the following questions using the result of part b.

c. What are the independent variables (x) and what is the dependent variable(y)?

d. Explain the meanings of the regression parameters.

e. What is the meaning of the intercept?

f. Write the least squares estimation line for this question?

g. If the strike outs for an NL team was 40, errors were 2, and had 3 wins and 2 losses, what would you predict the winning percentage to be?

h. What is the 95% confidence interval for the average winning percentage of an NL team that has 40 strike outs, 2 errors, and 3 wins and 2 losses?

i. What is the 95% prediction interval for the winning percentage of an NL team that has 40 strike outs, 2 errors, and 3 wins and 2 losses?

j. Find the adjusted multiple coefficient of determination and interpret your results.

k. Find the multiple correlation coefficient and interpret your results.

l. Is there multicollinearity between your independent variables? Is this a problem?

m. Check ALL the assumptions for regression analysis. If one of the assumptions is not satisfied, what would you recommend as a solution?

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