Multiple Linear Regression Paper

The price determinants of the EastVille Market

The objective of this paper is to estimate the selling price of houses in the Eastville market by using a multiple linear regression model based on the following possible predictors:

• SQ_FT (total square feet in the house)
• BEDS (number of bedrooms)
• BATHS (number of bathrooms)
• HEAT (0=gas forced air heating; 1=electric heat)
• STYLE (the architectural style of the home: 0=trilevel, 1=two-story, 2=ranch-styled)
• GARAGE (number of cars that can fit in garage)
• AGE (age of the home in years)
• FIRE (0=no fireplace present; 1=at least 1 fireplace present)
• BASEMENT (0=no basement; 1=basement)
• PRICE (selling price in thousands)
• SCHOOL (0=Eastville school district; 1=Apple Valley school district)

For this purpose, a dataset that contains all above variables (plus the house selling price) with 108 cases is available.

Observe that we need two dummy variables to represent the variable STYLE (we will use Trilevel as the baseline):

Price: $9.47

Solution: The downloadable solution consists of 5 pages, 447 words and 2 charts.
Deliverable: Word Document