Question

1. First City Real State has built a multiple regression model to predict the sales price of homes in a certain subdivision. The multiple regression output based on 319 residential properties is given below. The independent variables are:

X1 = Home Size (in square feet)

X2 = Age of the house

X3 = Number of bedrooms

X4 = Number of bathrooms

X5 = Garage size (# of cars)

Regression Statistics

Multiple R	.90337182
R Square	.81608064
Adjusted R Square	.81314263
Standard Error	27350.2517
Onservations	319

Anova

	Coeffiecients	Standard Error	T Stat	P-Value
Intercept	31127.6023	9539.669039	3.26296	.00122
Sq Feet	63.0656426	4.017033409	15.6996	2.3E-41
Age	-1144.4367	112.7800042	-10.148	4.2E-21
Bedrooms	-8410.3789	3002.511183	-2.8011	0.00541
Bathrooms	3521.95402	1580.996836	2.22768	0.02661
Garage #	28203.5419	2858.692416	9.86589	3.6E-20

Anova

	df	SS	MS	F	Significance F
Regression	5	1.0389E+12	2.1E+11	277.767	9.0374E-113
Residual	313	2.34135E+11	7.5E+08
Total	318	1.27303E+12

Use the above EXCEL regression output to answer the following questions.

1. Formulate the price function based on the above regression output.

2. Use the above equation to estimate the price for a 15-year-old house with 2580 square feet of living area that has 4 bedrooms with 2.5 baths and two car garages.

3. Is this regression model good for forecasting purposes? Explain.

4. What percentage of variation in price is explained by use of independent variables in this regression? Explain.

5. Rank the independent variables based on their degree of contribution to the model (highest to lowest.)

6. What is the price difference between two homes with identical square footage, number of beds, number of baths, and garage size, but 5 years of age difference?

Answer 1