Develop a model to predict the asking price of houses in Silver Spring, Maryland, based on living space, lot size, whether the has a fireplace, the number of bedrooms, the number of bathrooms, age, whether it has central air conditioning, the number of parking spaces, and whether the house has a brick exterior. Use the sample of 61 houses that is stored in SilverSpring as the data for this analysis.

a. Using all the data as a training sample, develop a regression tree model to predict the asking price of the house.

b. What conclusions can you reach about the asking price of the house?

c. Using half the data as the training sample and the other half of the data as the validation sample, develop a regression tree model to predict the asking price of the house.

d. What differences exist in the results of (a) and (c)?