Question
In this Regional Sample, the response variable (y) is the median listing price and the predictor variable (x) is the median square feet. This graph
In this Regional Sample, the response variable (y) is the median listing price and the predictor variable (x) is the median square feet. This graph shows us that as the listing price of the house increases as the square footage of the house is increased. There is a mostly even split of homes above the line and below the line as well a few that fall on the line. Overall the represents a trend that can predict the listing prices of homes based off of square footage pretty accurately.
No outliers were recognized in the data. Outliers that could skew the data can come from counties with a generally higher living expense and higher end living. This means that the square footage is essence has more value so it would cost more which would be taken into consideration when pricing in those counties.
Using the equation y = 89.537x+65538.235 if you were to have 1800 square feet home
the predicted listing price for this home would be around $226,705. This prediction can be proven by plugging 1800 in the place of x in our equation. This is what that would look like 89.537(1800)+65538.235=226,704.835.
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