A sample of houses for sale in California was taken. Data were collected on the asking price of the house, size of the house (in square feet), number of bathrooms, and number of bedrooms. Below is the output from the multiple linear regression analysis: Estimate Std. Error t value pr(>It!) (Intercept) -28.01 190.84 -0.147 0.8844 Beds -270.94 89.78 -3.018 0.0056 Baths 589.54 91.76 6.425 0.0000008 Note: "Beds" is the number of bedrooms in the house, and "Baths" is the number of full bathrooms in the house. "Price" is in thousands of dollars.\f5. Making predictions. a. Suppose a new house goes on the market that has 1700 square feet, 3 bedrooms, and 2.5 bathrooms. If I wanted to estimate the price for this house using an interval, would you use a prediction interval or a confidence interval? Explain. b. What would the point estimate for the selling price of the house that has 1700 square feet, 3 bedrooms, and 2.5 bathrooms be? Be sure to include units. C. There was a house in the sample with 1700 square feet, 3 bedrooms, and 2.5 bathrooms, and the observed selling price was $415,000 (Price = 415). Calculate the residual for this observation. Did the model overestimate or underestimate how much it would sell for? Circle the residual for this house on the two residual plots (you should circle one residual on each plot). d. Calculate the 95% confidence interval for the Ba, the slope coefficient for the number of bedrooms. Show setup/work. e. Write the two-part write-up for the confidence interval from part (d), in context