Statistics Paper - Condosales
Case Study: Condo Sales Case
A. Brief introduction to the case
This case involves an investigation of the factors that affect the sale price of oceanside condominium units. The sales data were obtained for a new oceanside condominium complex consisting of two adjacent and connecting eight-floor buildings. The complex contains 200 units of equal size (approximately 500 square feet each).
Variables • Dependent variable 1. Sale price: Y
• Independent variables 1. Floor height: x1 2. Distance from elevator: x2 3. View of the ocean: x3 4. End unit: x4 5. Furniture: x5
Issues identified • To build a regression model that accurately predicts the sale price of a condominium unit sold at auction. • Use graphs to demonstrate how each of the independent variables in the model affects price.
B. Case Analysis
1. Creating the deterministic regression model
Since there are five independent variables, there shall be 5 variables in our initial first order regression model:
E(y) = β0 + β1x1 + β2 x2 + β3 x3 + β4 x4 + β5 x5 + (
Using the data in the CD, we find the SPSS printout for the case as below:
Coefficients (a)
|Model | |Unstandardized Coefficients |Standardized |t |Sig. |
| | | |Coefficients | | |
| | |B |Std. Error |Beta | |
| | |B |Std. Error |Beta |
|1 |.789(a) |.623 |.604 |21.324 |
a Predictors: (Constant), distview, floorsq, furnish, endunit, distance, floorview, floordist, view, distsq, floor
ANOVA(b)
Model | |Sum of Squares |df |Mean Square |F |Sig. | |1 |Regression |147387.091 |9 |16376.343 |35.614 |.000(a) | | |Residual |91505.684 |199
A. Brief introduction to the case
This case involves an investigation of the factors that affect the sale price of oceanside condominium units. The sales data were obtained for a new oceanside condominium complex consisting of two adjacent and connecting eight-floor buildings. The complex contains 200 units of equal size (approximately 500 square feet each).
Variables • Dependent variable 1. Sale price: Y
• Independent variables 1. Floor height: x1 2. Distance from elevator: x2 3. View of the ocean: x3 4. End unit: x4 5. Furniture: x5
Issues identified • To build a regression model that accurately predicts the sale price of a condominium unit sold at auction. • Use graphs to demonstrate how each of the independent variables in the model affects price.
B. Case Analysis
1. Creating the deterministic regression model
Since there are five independent variables, there shall be 5 variables in our initial first order regression model:
E(y) = β0 + β1x1 + β2 x2 + β3 x3 + β4 x4 + β5 x5 + (
Using the data in the CD, we find the SPSS printout for the case as below:
Coefficients (a)
|Model | |Unstandardized Coefficients |Standardized |t |Sig. |
| | | |Coefficients | | |
| | |B |Std. Error |Beta | |
| | |B |Std. Error |Beta |
|1 |.789(a) |.623 |.604 |21.324 |
a Predictors: (Constant), distview, floorsq, furnish, endunit, distance, floorview, floordist, view, distsq, floor
ANOVA(b)
Model | |Sum of Squares |df |Mean Square |F |Sig. | |1 |Regression |147387.091 |9 |16376.343 |35.614 |.000(a) | | |Residual |91505.684 |199