Problem 1.
Using the data set on the Excel regression tool provided, create a regression model capable of forecasting 5-year average return (DV) of a mutual fund with expense ratio, net asset value, and Morningstar rank. Morningstar rank has been recoded into an indicator variable. (2 and 3 star ranks are coded 0, 4 and 5 star ranks are coded 1).
a. Examine the effects of each independent variable (IV) on the dependent variable. If you had to select only one IV, what would it be?
b. Using all 3 IVs, what is your % of explained variability?
c. What is the regression equation, using all 3 IVs, to predict 5-year average return?
d. Using all 3 IVs: Your particular fund has a star ranking of 4-star (coded 1), a net asset value of 68.11, and an expense ratio of .63. Estimate your 5-year average return with 95% confidence.
e. Using all 3 IVs: What is your estimate of the mean 5-year average return for all funds as described in part d (4-star, NAV 68.11, expense ratio .63)? Use 95% confidence.
f. Using all 3 IVs: Is this model significant overall? Be sure to support your answer with specific results.
g. Remove any insignificant variables. What variables did you remove? What variable(s) is/are left?
h. After removing all insignificant variables, do you suspect any problems due to collinearity? Yes/No. Briefly explain.
Problem 2. Remember, you may use the exam Excel tools (and anything in our Sakai course).
A company is planning a plant expansion. They can build a large or small plant. The payoffs for the plant depend on the level of consumer demand for the company’s products. For the large plant, the company expects $85 million in profit if demand is high and $35 million if demand is low. For the small plant, the company expects $54 million in profit if demand is high and $19 million if demand is low.
The company believes that there is a 72% chance that demand for their products will be high and a 28% chance that it will be low.
Construct a payoff matrix based on the given information. Remember, you may use the exam Excel tools
Payoff
What is the decision according to the EMV criterion? What is the MAX EMV?
What is the EVPI and what does this indicate? What does it suggest about this decision being made?
Is your decision above ‘sensitive’ to the probability of HIGH demand? In other words, would your decision change if the probability of HIGH demand were less? Yes/No. Briefly explain and support your answer.
The company may conduct a survey to make a better prediction of demand for their products. The following probability information has been provided regrading survey outcomes that might help to predict the probability of demand.
High Demand Low Demand Total
Favorable .66 .10 .76
Unfavorable .06 .18 .24
Total .72 .28 1
Using these probabilities and the payoffs, complete the decision tree model in your Excel file. Input the correct information into the GREEN cells. The spreadsheet will calculate all EMVs for you once you input the needed information.
Please add a screen shot of your completed tree here:
What would be the EMV assuming that the survey is done? How does this EMV compare to your MAX EMV on the payoff table, that is, the EMV without the survey?
Should the market research firm be hired at a cost of $100,000? Is the survey helpful in this case? Be sure to support your answer with specific results from your models.