Use theHousePricedata and via multiple regression select the three variables that predict the house selling price the best. Make another table with these three variables and answer the questions.Numerical answers are rounded so choose the answer that matches the best:

9. Identify the largest coefficient. What is its value and what is the interpretation of this number? (Choose the most appropriate answer. Note: numbers are truncated.)

• a. 4.17; This is the amount in thousands that the selling price of a new house will increase.
• b. 4.17; This shows how much the selling price correlates with the age of the house
• c. 4.411; The older the house, the higher the selling price by this amount in thousands.
• d. 4.411; For each additional bathroom the selling price of the house increase by 4.41 hundred thousand dollars.

10. Which of the three variables has the second best P-value and what is this P-value? (Note: numbers are truncated.)

• a. Age has second best P-value; P-value = 0.018
• b. The higher the P-value, the better; so a P-value = 0.043 is very good.
• c. #Rooms has second best P-value; P-value = 0.043
• d. All P-values are poor since they are all below 5%.

11. Using this second table predict the selling price of a housethat is 10 years old, has 2 bathrooms and 3 rooms.

• a. 10.5 hundred thousands
• b. 12.93 hundred thousands
• c. 9.06 hundred thousands
• d. 13.2 hundred thousands

12. What is the Y intercept and its interpretation?

• a. 1.267;This number is used to predict the selling price of the house based on the variables.
• b. 0.837;This number shows how much the house selling price correlates with the other variables.
• c. -1.437; It has no meaningful interpretation in this case.
• d. -1.43; This is how much the new house price will decrease.

https://www.limes.one/Content/DataFiles/HousePrice.txt