Section 4.2: Least-Squares Regression Goal: Fit a straight line to a set of points as a way to describe the relationship between the X and Y variables.

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Section 4.2: Least-Squares Regression Goal: Fit a straight line to a set of points as a way to describe the relationship between the X and Y variables.

Asking price in thousands of dollars Dec.2010 data, Naples, FL. (Problem 28 in text)

Residual = Observed - Predicted

Interpretation SLOPE For each one square foot increase, we expect the average asking price to be higher. ( = $68.60) INTERCEPT A zero square foot home would have an asking price of ( =$83,236.60) This example of the intercept is extrapolation. It is a bad idea to extrapolate outside of your range of data.

How to calculate predicted value and residuals Suppose X=1344 square feet, Y= × 1344 = Predicted Y= Predicted Cost= $175,435 Residual = observed – predicted = – = 4.565

An example of leverage

For a good applet to explore leverage and correlation see: