What is the code that I would write into R Studio to figure out these problems?

- Baths: The number of bathrooms - Beds: The number of bedrooms - Area: The livable area, in square feet - Fireplaces: The number of fireplaces - Garage_Cars: The number of cars that fit in the garage - Year_Built: The year the home was built - Sale_Price: The sales price of the home, in dollars - Central_Air: Indicates if there is a central air conditioner: yes (Y), no (N) - Garage_type: Type of garage in the house: attached (Attchd), other (Other, indicating basement, built-in, carport, detached, etc), and no garage (No_Garage) You will need to recode Central_Air from Y and N to 1 and 0, respectively. You will also need to code Garage_type appropriately for use in regression. Answer each question below using full sentences: do not paste in R output. Upload a PDF of your responses as well as a separate R script that replicates the calculations you have done. In this assignment, R functions will be given so you can plot valuable information from data. You don't need to paste the plot on your report. 5. Create a model for the sales price of a home as a function of the livable area, the number of bedrooms, the number of fireplaces, the year it was built, garage type, and if it has an air conditioner or not. Call this Model 4. Is the coefficient for Central_Air statistically significant in Model 4? Practically significant? Compare your findings here to the coefficient of Central_Air in Model 1: why is the coefficient of Central_Air so different here? 6. Interpret each coefficient of the garage type dummy variables in Model 4. For homes of a given area, bedrooms, fireplaces, age, and air conditioner status, which of the three possible garage types corresponds to the highest estimated home prices? The lowest? 7. Suppose a new client comes to you wanting to sell their 2005-built, 3 bedroom, 1 fireplace home, with an area of 2000 square feet and a central air conditioner but no garage. Use Model 4 to make your best prediction for the sales price of this house. What would be a reasonable range to expect the actual sales price to be in with high (95\%) confidence