Reference no: EM131489916

CASE - HOUSING PRICE STRUCTURE IN MID CITY

Sales of single-family houses have been brisk in Mid City 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. The file C10_02.xisx contains data on 128 recent sales in Mid City. 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. I and 2 are more traditional neighborhoods whereas neighborhood '3 is a more prestigious neighborhood.

Use regression to estimate and interpret the pricing structure of houses in Mid City. Here are some considerations.

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, neighborhoods 1 and 2 be collapsed into single "older" neighborhood?

Exercise Problems

For this assignment complete the exercises below. Show enough of your work so that your instructor can follow your logic.

The owner of the Original Italian Pizza restaurant chain would like to predict the sales of his specialty, deep-dish pizza. He has gathered data on the monthly sales of deep-dish pizzas at his restaurants and observations on other potentially relevant variables for each of his 15 outlets in central Indiana. These data are provided in the file P10_04.xlsx.

a. Estimate a multiple regression model between the quantity sold (Y) and the explanatory variables in columns C -E.

b. Is there evidence of any violations of the key assumptions of regression analysis?

c. Which of the variables in this equation have regression coefficients that are statistically different from zero at the 5% significance level?

#18 (Pg 555) The Undergraduate Data sheet of the file ei0-21.xlsitontains information on 101 undergraduate business programs in the U.S., including various rankings by Business Week. Use multiple regression to explore the relationship between the median starting salary and the following set of potential explanatory variables: annual cost, full-time enrollment, faculty-student ratio, average SAT score, and average ACT score. Which explanatory variables should be included in a final version of this regression equations? Justify your choices. Is multicollinearity a problem? Why or Why not?

#23 (Pg 560) The Undergraduate Data sheet of the filen(L21.xlsx contains information on 101 undergraduate business programs in the U.S., including various rankings by BuSiness Week. Use forward, backward, and stepwise regression analysis to explore the relationship between median starting salary and the following set of potential explanatory variables: annual cost, full-time enrollment, faculty-student ratio, average SAT score, and average ACT score. Do these three methods all lead to the same regression equation? If not, do you think any of the final equations are substantially better than any of the others?

Attachment:- Data.rar