Question

Data Island is a beautiful beach resort. It has been a premier touristic destination for over 80 years, and the earliest holiday homes have been built just a few steps away from the beach itself. You work for a real estate agency that sells and rents holiday homes in Data Islands. Recently, one of the interns working on the Data Science Team presents you a report. According to the linear regression model they have run, the price of a house is proportional to its age. But this does not make any sense: after all, newer houses should be worth more.
1. Comment the regression output, noticing that the Sig. column is reporting the p-value of the coefficient. What can you tell about the sign of Age’s slope?
2. Why do you think the model forecasts an higher price for old holiday homes? Think carefully about all the information you have concerning Data Island.
3. Assuming that more data and variables are available, what would you suggest your intern to do to fix their report?

Answer 1

A lovely beach resort is Data Island. Since it first became a popular tourist destination more than 80 years ago, the earliest vacation homes have been constructed just a few steps from the beach. You are a real estate agent for a company that deals in vacation rentals and sales in the Data Islands. A report was recently presented to you by one of the interns on the data science team. The age of a house affects its price, according to the linear regression model they have ran. But this is absurd because newer homes ought to be more valuable.

1.  As the coefficient of Age is positive, so the older the house, the more price it is. Sig. (or p-value) = 0.000 which implies  that the co-efficient of Age is significant.

2.  This may be that the people who live there are reluctant to sell it, so they quote a higher price or the size of old houses are large or they may be located at prime location.

3.  We would ask new interns to consider other independent variables for the regression equation like size of house, location, etc.

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