PLEASE ANSWER USING "R" CODES AND A PROPER EXPLANATION FOR THE RESULTS

The data file houseprice.csv contains data on 1000 house sales in Colorado. The variables are price: Price is in $ sqft: Total square footage of the house bedrooms: Number of bedrooms bath: Number of baths age: Age of the house in years owner: =1 if occupied, 0 if vacant ranch: =1 if the house is ranch styled fireplace: =1 if the house has a fireplace mountain: =1 if the house is on the mountain

(a) Compute summary statistics on the dataset and comment. In particular, construct a histogram of price. What do you observe?

(b) Estimate a regression model explaining ln(price/1000) as a function of the remaining variables. Divide the variable sqft by 100 prior to estimation. Comment on how well the model fits the data. Discuss the signs and statistical significance of the estimated coefficients. Are the signs what you expect? Give an exact interpretation of the coefficient of the mountain.

(c) Create a variable that is the product of mountain and ranch. Add this variable to the model and re-estimate. What is the effect of adding this variable? Interpret the coefficient of this interaction variable, and discuss its sign and statistical significance.

(d) It is arguable that the ranch-style homes may have a different regression function from the diverse set of other styles possible. Carry out a Chowtest of the equivalence of the regression models for ranch versus non-ranch styles. What do you conclude?

(e) Using the equation estimated in part (d), predict the value of a ranch-style house with 2500 square feet of area, that is 20 years old, that is owner-occupied at the time of sale, that has a fireplace, 3 bedrooms, and 2 baths, and that is not on a mountain.