Making Decisions Based on Demand and Forecasting

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Making Decisions Based on Demand and Forecasting
Greg Wells
Professor Dr. E.T. Faux
Managerial Economics and Globalization
October 20, 2012

1. Report the demographic and independent variables that are relevant to complete a demand analysis providing a rationale for the selection of the variables. The independent variables for this report will be population, average income per household, age of population, and the price of pizza. A key determinant of demand is the population of the area in question and as we will see in this report, growth will play a positive factor. The ultimate concern is can the city sustain another pizza delivery entity at its current population to restaurant ratio? Level of income is relative to demand and typically the higher the household income the higher the demand for a product. Income levels also can influence the marginal profit for the company. Areas with higher income are willing to pay extra for a convenience of pizza delivery and typically hold more parties where pizza delivery is used as opposed to friends bringing food. Finally yet importantly is price. Given the city’s below average median house income compare to the nation, price was selected as a variable. The dependent variable is the number of pizzas sold in the area. Using a regression model we will input the dependent variable; value of pizzas sold, along with the independent variables; population, median household income and price. We then will look then look at the summary output from the regression to make some decision about bringing a pizza store to our city. 2. Using Excel or other calculation software, input the data you collected in criterion one to calculate an estimated regression. Then, from the calculation provided, interpret the coefficient of determination, indicating how it will influence your decision to open the pizza business. Explain any additional variables that may improve the coefficient of...
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