1. 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.

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

5. Residual = Observed - Predicted

7. Interpretation SLOPE For each one square foot increase, we expect the average asking price to be 0.0686 higher. (0.0686 = $68.60) INTERCEPT A zero square foot home would have an asking price of 83.2366 (83.2366 =$83,236.60) This example of the intercept is extrapolation. It is a bad idea to extrapolate outside of your range of data.

8. How to calculate predicted value and residuals Suppose X=1344 square feet, Y= 180.0 83.2366 + 0.0686 × 1344 = 175.435 Predicted Y= Predicted Cost= $175,435 Residual = observed – predicted = 180.0 – 175.435 = 4.565

9. An example of leverage

10. For a good applet to explore leverage and correlation see: http://www.calpoly.edu/~srein/StatDemo/All.html