A tax assessor wishes to develop a model to be used for estimating

the value of a home for determining property taxes. A local real estate firm provided a data set (columns C6 thru C21 ) in a particular development area (location, location, location) containing the predictor variables:
 
 
                        
Predictor Variables                                                             
1)    Value: Most recently  assessed home value (Y=Value)   
2) Acreage: Area of lot in acres
2)   stories: number of stories the home has
3)   Area: Square footage
4)   Exterior:  1=Exterior in excellent or good condition,    0=Exterior in average or poor condition.
5)   NatGas: 1= has natural gas heat (the most desirable),   0 = other heating system 
6)   Rooms: total number of rooms
7)   Bedrooms: Number of bedrooms
8)   FullBath: number of full bathrooms
9)   Halfbath: number of half bathrooms
10) Fireplace: 1=Yes 0 = No
11) Garage: 1=Yes 0=No
12) Area**2: Area of home squared
13) Acreage**2: Acreage of lot squared
14) Stories**2: number of stories the home has squared
15) Rooms**2: total number of rooms squared
 


A) Based on these Best Subsets results, use the various selection criteria discussed in class to recommend two or 3 potential models for further evaluation.   
 
B) Discuss the limitations of this Best Subsets selection algorithm. Why shouldn't you just recommend a single model and be done with it?


C) Based on your potential models, and additional analysis, recommend a model. Discuss why you chose this particular model.