quiz 06, due June 30 at the beginning of class Name: You analyst runs the follow
ID: 3371179 • Letter: Q
Question
quiz 06, due June 30 at the beginning of class Name: You analyst runs the following 1-variable regression of House Price vs.# of Rooms SUMMARY OUTPUT Multiple R R Square " Lower SSS Upeer 951 99,841 159 Question 1: Using 1 variable regression house price vs. number of rooms predict the price of a house with 4 rooms for your client. If the actual price is $135,000 what is the error? You enhance the model by adding 2 more variables SUMMARY OUTPUT Multiple R R Square 0.6613 21,780 89,857 45,000 200 (1,000) 1456 (3.183) 49) 11,647) Rooms 11.02% 314 353) Age of House Square Ft Question 2: What is the predicted house price for a home based on the new model with 3 variables, given that the house has 4 rooms is 5 years old and is 2,000 Square feet? If the actual price is $140,000 what is the error? Question 3: which coefficients are statistically significant at alpha (a) 5% in the model with 3 variables Question 4: What is the 95% confidence interval for Square Ft. coefficient? Question 5: which of the two models (house price vs. # Rooms or House Price vs. "Rooms, Age and square ft.) will you choose to use and why?Explanation / Answer
Solution1:
from regression output
regression eq is
house_price=50000+20000*no of rooms
but given no of rooms=4
substitute in Regression eq to get the house price
house_price=50000+20000*4
predicted house price=130000
Error=Actual -predicted
=135000-130000
=5000
Error=5000
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