1. A linear regression model was built to predict the housing price (dependent variable) using the following independent variables (predictors):

• renovated_no: =1 if the house was not renovated, =0 otherwise;
• zipcode_98003: =1 house is located in zipcode 98003, =0 otherwise;
• zipcode_98007: =1 house is located in zipcode 98007, =0 otherwise;
• bedrooms: Number of bedrooms;
• bathrooms: Number of bathrooms;
• lot: Land space, which is converted using z-transformation;
• sqft_above: The square footage of the interior housing space above ground level;
• sqft_basement: The square footage of the interior housing space below ground level;

1. The model specification for the house price (dependent variable) is:

806967.904-238559.094 renovated_no-82677.643 zipcode_98003 + 101088.776 zipcode_98007-46967.721 bedrooms + 6016.794 bathrooms + 7980 lot + 298.285 sqft_above + 188.592 sqft_basement

Assume all predictors are significant. Based on the information above, answer the following questions. Show the steps how you obtain the result for all questions below for full credits.

For two houses, one that has 2,000 sqft interior space above ground and 1,000 sqft basement and the other that has 1,900 sqft interior space and 1,150 sqft basement, assuming the other conditions of the two houses are the same, which one has the higher expected price and what is the difference? Show the steps how you obtain the result.

How tointerpret the coefficient of variable "lot"? What does it mean when lot=0? Can thevalues of variable"lot"be negative and why or why not?