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book PS2_solutions - Rose-Hulman Institute of Technology...

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Rose-Hulman Institute of Technology / Department of Humanities & Social Sciences / K. Christ Fall Quarter, 2009 2010 / SL 351, Managerial Economics; EMGT 531, Economics for Technical Managers Problem Set 2 -- Solutions Textbook Problems : Hirschey, Chapter 3: P3.5, P3.7, P3.8, P3.10 Hirschey, Chapter 4: P4.6, P4.7, P4.8, P4.9, P4.10 Hirschey, Chapter 5: P5.3, P5.8, P5.9, P5.10 Hirschey, Chapter 6: P6.3, P6.4, P6.5, P6.6, P6.8 Extra Problems : 1. Diversified Products. You are the manager of a diversified products firm that received revenues of $30,000 per year from product X and $70,000 per year from product Y. The own-price elasticity of demand for product X is 2.5, and the cross-price elasticity of demand between product X and product Y is 1.1. How much will your firm s total revenues (combined revenues from both products) change if you increase the price of good X by 1%? 2. Kodak. You are a manager in charge of monitoring cash flow at Kodak (in 2002). Traditional photography equipment comprises 80% of Kodak s revenues, which grow about 2% annually. You recently received a preliminary report that suggest consumers take three time more digital photographs than photos with traditional film, and that the cross-price elasticity of demand between digital and disposable cameras is 0.2. Over the last several years, Kodak has invested over $5 billion to develop and begin producing digital cameras. If the own price elasticity of demand for disposable cameras is -2.5, how will a 1% decrease in the price of disposable cameras affect Kodak s overall revenues from both disposable and digital camera sales? 3. Use data set „rhit pizza‟. This data set contains hypothetical demand data for an unidentified pizza supplier to the Rose-Hulman campus over the 2001-2002 academic year. a. Regress Q1 on P1 and P2. b. Regress lnQ1 on lnP1 and lnP2. Interpret your regression results in light of this data transformation. c. Plot Q1 against the date variable and see if you can determine what other factors might usefully be included as explanatory variables in a demand model. d. Modify the regression specification from part (b) to include the following additional right hand side variables: FINALS, INSESSION, MON, TUE, WED, THU, FRI, SAT (not SUN!). Compare your results with those you obtained in part (b).
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4. Use data set „export good‟. This data set contains hypothetical historical sa les data (variable q1) for an unspecified producer that sells in both domestic and foreign markets. a. Generate a chart showing the month/year on the horizontal axis and q1 on the vertical axis. b. Regress q1 on the trend variable. c. Regress lnq1 on lnp1, lnp3, lnip, and lntwex. d. Regress lnq1 on lnp1, lnp3, lnip, lntwex, and trend. e. Comment on your regression results. 5. Use the following data to generate the forecast MAPE, RMSE, and FA: Month Forecast Actual January 4,532 4,268 February 4,634 4,573 March 4,737 5,024 April 4,839 5,214 May 4,942 4,969 June 5,044 5,121 July 5,147 4,898 August 5,249 5,047 September 5,352 5,136 October 5,454 5,372 November 5,556 5,702 December 5,659 5,821
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P3.5 A. The demand faced by CPC in a typical market in which P = $10, Pop = 1,000,000 persons, I = $60,000, and A = $10,000 is: Q = 5,000 - 4,000P + 0.02Pop + 0.25I + 1.5A = 5,000 - 4,000(10) + 0.02(1,000,000) + 0.25(60,000) + 1.5(10,000) =15,000 B.
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