Business statistics data analysis | Operations Management homework help
Data Analysis & Decision Making For Managers
Case 1: Quantity Discounts at Petri Chair Company
The Petri Chair Company manufactures customized wood furniture and sells the furniture in large quantities to major furniture retailers. Adolph Westphalia has recently been assigned to analyze the company’s pricing policy. He has been told that quantity discounts were usually given. For example, for one type of chair, the pricing changed at quantities of 200 and 400—that is, these were the price breaks, where the marginal cost of the next chair changed. For this type of chair, an attached file contains the quantity and total price to the customer for 81 orders. Use regression to help Adolph discover the pricing structure that Petri Chair evidently used. (Note: A linear regression of Total Price versus Quantity will give you a “decent” fit, but you can do much better by introducing appropriate variables into the regression.)
Data Set 1 in attached Excel document sheet 1
Case 2: Housing Price Structure in Aberdeen
Sales of single-family houses have been brisk in Aberdeen this year. This has especially been true in older, more established neighborhoods, where housing is relatively inexpensive compared to the new homes being built in the newer neighborhoods. Nevertheless, there are also many families who are willing to pay a higher price for the prestige of living in one of the newer neighborhoods. An attached file contains data on 128 recent sales in Aberdeen. For each sale, the file shows the neighborhood (1, 2, or 3) in which the house is located, the number of offers made on the house, the square footage, whether the house is made primarily of brick, the number of bathrooms, the number of bedrooms, and the selling price. Neighborhoods 1 and 2 are more traditional neighborhoods, whereas neighborhood 3 is a newer, more prestigious neighborhood.
Use regression to estimate and interpret the pricing structure of houses in Aberdeen.
Here are some questions that you need to answer after performing the regression analysis:
1. Do buyers pay a premium for a brick house, all else being equal?
2. Is there a premium for a house in neighborhood 3, all else being equal?
3. Is there an extra premium for a brick house in neighborhood 3, in addition to the usual premium for a brick house?
4. For purposes of estimation and prediction, could neighborhoods 1 and 2 be collapsed into a single “older” neighborhood?
DATA SET 2 in attached Excel document sheet 2
