Please help me solve this problem. All the data is in the file pamsue.xls, find attached also the Table A & B that is referred to in question 2 & 3.
This case involves the decision to locate a new store at one of two candidate sites. The decision will be based on estimates of sales potential.
Use the final regression equation in the last step to predict sales at the two sites under consideration. (Attached document called "hints" can be used for this)
How would you describe the type of location sites that are likely to have higher sales?
A group within the planning department had previously developed a subjective approach in which potential sites are classified according to an assessment of the "competitive type" of the trading zone. Below in table A, the 7 "competitive types" are defined. How good is this classification method at predicting sales? How can you quantify this?
Two sites, A and B, are currently under consideration for the next new store opening. Characteristics of the two sites are provided below in table B. Which site would you recommend? Justify you choice and give the best sales forecasts you can. You may use the subjective classifications from question 2 along with any other variables you think will give the best forecast. Give some estimates of the accuracy of the forecasting method you use and any other limitations of the forecasting method.
Two of the variables in the data base are under managerial control: the size of the store (square feet of selling area) and the percentage hard goods stocked in the store. Margins on hard goods (house wares, appliances, stationary, drugs) are different from margins on soft goods (clothing, for example). Do either of these factors appear linked to sales? If so, describe the link you observe and the managerial implications.
Technical: After developing your regression model, check to make sure the technical assumptions are satisfied. Comment on any points that would concern you based on the diagnostics.
The solution provides step by step method for the calculation of multiple regression analysis. Formula for the calculation and Interpretations of the results are also included.