1. Chapters 8 Linear Regression

2. Correlation and Regression Correlation = linear relationship between two variables. Summarize relationship with line. Called Regression line. Explanatory variable (x) Response variable (y)

3. Regression line Explains how response variable (y) changes in relation to explanatory variable (x). Use line to predict value of y for given value of x.

4. Regression line Need mathematical formula. Different lines by sight. Predict y from x. The _________ values are called _______  ___________________________________ The _________ values are called _______  ___________________________________

5. Regression line Look at vertical distance Error in regression line. Place line to make these errors as small as possible.

6. Least squares regression Most commonly used regression line. Puts line where sum of the squared errors as small as possible. Minimizes ______________ Based on statistics

7. Regression line equation where

8. Regression line equation - slope b 1 = _________________. Interpretation: ________________________________ ________________________________ ________________________________ Very important for interpreting data.

9. Regression line equation – intercept b 0 = _________________________ Interpretation: _______________________________________ Usually not important for interpreting data.  Values of x are usually not close to 0.

11. Calculating the regression line. Degree Days vs. Gas Usage

12. Calculating the regression line. Don’t forget to write the equation.

13. Interpretations Interpretation: Slope Interpretation: Intercept

14. Prediction Use regression equation to predict y from x. Ex. Predicted gas consumption when degree days = 43? Ex. Predicted gas consumption when degree days = 24?

15. Residuals Calculate Ex. when degree days = 43? Ex. when degree days = 24?

16. Residuals Variation in y not measured by regression line. Residual for each data point. Mean of residuals _________________ _________________

17. Residual Plot Special Scatterplot Explanatory variable (x) on horizontal axis. Residuals (e) on vertical axis. Horizontal line at residual = 0. Good Residual Plot _________________

19. Example of Other Residual Plots

20. Properties of regression line Regression line always goes through point r is connected to the value of b 1.

21. Properties of R 2 Any value from _________________ ________________________________ Higher values of R 2 mean regression line ________________________________ Lower values of R 2 mean regression line ________________________________

22. Degree Days vs. Gas Usage r = 0.9953, R 2 = _______________ Interpretation:

23. Cautions about regression Extrapolation is risky Strong association between explanatory and response variables does not mean that explanatory variable causes response variable. Ex: High positive correlation between number of TV sets per person and average life expectancy.