1. PSY 1950 Regression November 10, 2008

2. Definition Simple linear regression –Models the linear relationship between one predictor variable and one outcome variable –e.g., predicting income based upon age Multiple linear regression –Models the linear relationship between more than one predictor variables and one outcome variable –e.g., predicting income based upon age and sex Lingo –Independent/dependent, predictor/outcome

3. History Astronomical predictions: method of least squares –Piazzi (1801) spotted Ceres, made 22 observations over 41 days, got sick, lost Ceres –Gauss: "... for it is now clearly shown that the orbit of a heavenly body may be determined quite nearly from good observations embracing only a few days; and this without any hypothetical assumption.” Genetics: Regression to the mean –Galton, F. (1886). Regression towards mediocrity in hereditary stature. Journal of the Anthropological Institute, 15, 246–263.

4. Lines Mathematically, a line is defined by its slope and intercept –Slope is change in Y per change in X –Intercept is the points at which the line crosses the Y-axis, i.e., Y when X = 0 Y = bX + a –b is slope –a is intercept

5. Which Lines is Best?

6. Residuals Residuals are –Errors in prediction –Difference between expected values (under your model) and observed values (in your dataset)

7. Minimizing Residuals Can define the best fit line by summing –Absolute residuals (Method of Least Absolute Deviations) –Squared residuals (Method of Least Squares)

8. Which is Better? Method of Least Squares –Not robust –Stable (line doesn’t “jump” with small changes in X) –Only one solution (unique line for each dataset) Method of Least Absolute Deviations –Robust –Unstable (line does “jump” with small changes in X) –Multiple solutions (sometimes) http://www.math.wpi.edu/Course_Materia ls/SAS/lablets/7.3/7.3c/lab73c.htmlhttp://www.math.wpi.edu/Course_Materia ls/SAS/lablets/7.3/7.3c/lab73c.html

9. Multiple Solutions Any line within the “green zone” produces the same summed residuals via the method of least absolute deviations

10. Method of (Ordinary) Least Squares

11. Regression Coefficients Slope Intercept

12. Standardized Coefficients ^ ^

14. Regression Line Passes Through (M X, M Y )

15. Correlation and Regression Statistical distinction based on nature of the variables –In correlation, both X and Y are random –In regression, X is fixed and Y is random Practical distinction based on interest of researcher –With correlation, the researcher asks: What is the strength (and direction) of the linear relationship between X and Y –With regression, the research asks the above and/or: How do I predict Y given X?

16. Goodness of Fit The regression equation does not reveal how well your data fit your model –e.g., in the below, both sets of data produce the same regression equation

17. Standard Error of Estimate The standard residual Why df = n - 2? –To determine regression equation (and thus the residuals), we need to estimate two population parameters Slope and intercept OR Mean of X and mean of Y –A regression with n = 2 has no df ^

18. Coefficicent of Determination (r 2 )

19. Partitioning Sums of Squares

21. Testing the Model # predictors n minus # model parameters n minus (1 + # predictors)

22. Online Applets Explaining variance –http://www.duxbury.com/authors/mcclellandg /tiein/johnson/reg.htmhttp://www.duxbury.com/authors/mcclellandg /tiein/johnson/reg.htm Leverage –http://www.stat.sc.edu/~west/javahtml/Regre ssion.htmlhttp://www.stat.sc.edu/~west/javahtml/Regre ssion.html Distribution of slopes/intercepts –http://lstat.kuleuven.be/java/version2.0/Apple t003.htmlhttp://lstat.kuleuven.be/java/version2.0/Apple t003.html