Reference no: EM131045161
Analytical Methods in Economics and Finance-
Deciding where to locate a new retail store is one of the most important decisions that a manager can make. The manager of the marketing team of a European Motor vehicle company selling its vehicles in Australia hires you as an expert in business analysis and wants you to help him select a location for a new show room. The manager informs you that he would like to use annual gross revenue as a measure of success. As the project commences, you decide to conduct a regression analysis and find that the determination of success depends on the following variables:
-Number of people living within 10 km of the showroom (PEOPLE)
-Mean income of the households within 10 km of the showroom (INCOME)
-Number of competitors within 10 km of the showroom (COMPTORS)
-Drive away price of a brand new model of a mid-size family sedan (PRICE)
A dummy variable (DIST) that takes a value of 1 if a showroom is located within 10 km from the CBD and 0 otherwise.
You randomly select 50 show rooms of the motor company located at various states of Australia and record the value of each of the variables listed above plus annual gross revenue (REVENUE). This data is provided in the file Assignment.data.
(i) Report the summary statistics of the quantitative variables selected as determinants of success of a motor vehicle showroom. Briefly explain the distributions of these variables and what these tell you about the underlying data.
(ii) Represent the regression model you want to estimate and provide clear justification of your preferred estimation method of the model.
(iii) Report your estimated model and explain how PEOPLE, INCOME, COMPTORS, PRICE and DIST affect the dependent variable.
(iv) Which independent variable(s) significantly explain the dependent variable? Show your work.
(v) Rewrite your estimated model with the coefficient estimates upto eight decimal points, wherever appropriate and as per your estimated regression output from excel. Now based on your estimated model, predict the gross annual revenue of a showroom of the company located 8 km far from the CBD and for the mean values of the variables PEOPLE, INCOME, COMPTORS and PRICE. What do you observe?
(vi) What is the coefficient of determination adjusted for degrees of freedom (R-2)? What do this statistics tell you about the regression equation?
(vii) If the manager tells you to use a completely different set of variables that could potentially explain sales revenue, how would you defend your current selection of the variables? Clearly show all required steps involved in the process of justifying your inference.
Word limit: Maximum 1500 words plus computer analysis.
Attachment:- Data.rar
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