ANALYSIS REPORT OF HOMES SOLD
To develop a multiple linear regression model to help the firm identify the average resale value of homes in the Fayeetville area, we have collected information on a random sample of 50 homes sold in the Fayettville area over the first 8 months of 2011. This information was obtained from the Fayettville Multi-Listing Realty Service and thus, our sample only includes homes listed or sold by agents and companies that belong to the service.
We put collected data into SAS system to help us generate an automatic result. We use six independent variables to analyze the selling price of home, which are respectively the heated area of house, the age of house, the lot size of house, a dummy variable with a value of 1.0 for a house located in the Northeast quadrant of the city, located in the Northwest quadrant of the city, and located in the Southwest quadrant of the city. After running the SAS system, we get a following model:
Pricei = 65,829 + 66.98 sqfti – 591.65 agei + 34,178 lotszi – 19,024 dnei –1,169.3 dnwi + 460.35 dswi.
For example, we may interpret the independent variable SQFT like this: on the average, within the range of observed values, on each 1 square feet increase in the average heated area of house, the selling price of home increases by $66.980, controlling for agei, lotszi, dnei, dnwi, dswi, OTHC. Similarly, the other five variables can be interpreted in the same way.
The sample plan is to analyze the observed values and to learn how the project is going on through studying the results. The status of the project is very important to help us make a good business decision in the future.
To make an overall test of significance analysis, we could make two hypothesizes: one is that all of the six independent variables equal to zero, the other is one or more of the six independent variables is/are not equal to zero. As the