Imagine a realtor is trying to decide how to price a home for a client. Realtors often base their decision on the prices of similar homes that sold recently. But what if the realtor has difficulty finding very similar homes?
If the realtor has data on the attributes and prices of recently sold homes, then she can use regression to estimate a price for her client.
In this example, price is the dependent variable. Features of the home such as size, year built, value of the land, cost of materials, and so on, would be independent variables.
The ideal regression for a realtor would have many more than two variables, representing many features of the homes. For our purposes however, we will focus on just one variable at a time.
We are going to get more practice with regression using some data on condo sales in two buildings in New York City.
If the realtor has data on the attributes and prices of recently sold homes, then she can use regression to estimate a price for her client.
In this example, price is the dependent variable. Features of the home such as size, year built, value of the land, cost of materials, and so on, would be independent variables.
The ideal regression for a realtor would have many more than two variables, representing many features of the homes. For our purposes however, we will focus on just one variable at a time.
We are going to get more practice with regression using some data on condo sales in two buildings in New York City.
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