A real estate agent in Athens used regression analysis toinvestigate the relationship between apartment sales prices and thevarious characteristics of apartments and buildings. The variablescollected from a random sample of 25 compartments are asfollows:
Sale price: The sale price of the apartment (in €)
Apartments: Number of apartments in the building
Age: Age of the building (in years)
Size: Apartment size (area in square meters)
Parking spaces: Number of car parking spaces in the building
Excellent building condition (Pseudo-variable): 1 if the conditionof the building is
excellent, 0 different
Good building condition (Pseudo-variable): 1 if the condition ofthe building is
good, 0 different
We have the following results of regression analysis with the OLSmethod:
Coefficientsa |
Model | Unstandardized Coefficients | Standardized Coefficients | t | Sig. |
B | Std. Error | Beta |
1 | (Constant) | 98308,606 | 22888,689 | | 4,295 | ,000 |
Apartments | 5776,999 | 1215,251 | ,344 | 4,754 | ,000 |
Age | -905,594 | 269,262 | -,111 | -3,363 | ,003 |
Size | 1237,643 | 142,945 | ,586 | 8,658 | ,000 |
Parking space | 2966,996 | 1313,465 | ,096 | 2,259 | ,037 |
Excellent | 52337,908 | 19957,138 | ,108 | 2,623 | ,017 |
Good | 5543,922 | 16714,509 | ,013 | ,332 | ,744 |
a. Dependent Variable: Sale price |
Questions:
1. State the estimated regression equation.
2. Comment on the importance of the regression rates.
3. Give the interpretation of the regression coefficients.
