1. Regression The basic problem Regression and Correlation Accuracy of prediction in regression Hypothesis testing Regression with multiple predictors

2. The Basic Problem How do we predict one variable from another? How does one variable change as the other changes? Cause and effect (can only be inferred if it makes theoretical sense)

3. An Example Effect of Dow Jones Performance on Darts performance (to what degree can Dow Jones predict Dart performance)Effect of Dow Jones Performance on Darts performance (to what degree can Dow Jones predict Dart performance)

4. The Data

5. Relationship can be represented by line of best fit

6. Why use regression? We may want to make a prediction. More likely, we want to understand the relationship. X XHow fast does Darts rise with one unit rise in Dow Jones?

7. Regression Line Formula X X = the predicted value of Y (Darts) X XX = Dow value

8. Regression Coefficients “Coefficients” are a and b b = slope (also called rate of change) X XChange in predicted Y for one unit change in X a = intercept X Xvalue of when X = 0

9. Calculation SlopeSlope InterceptIntercept

10. For Our Data b = 11.13/5.43 = 2.04b = 11.13/5.43 = 2.04 a = 14.52 - 2.04*5.95 = 2.37a = 14.52 - 2.04*5.95 = 2.37 See SPSS printout on next slideSee SPSS printout on next slide

11. SPSS Printout for one Predictor R 2, Percentage of Variance

12. SPSS printout cont. Intercept Slope Is regression Significant? Error of predictio n

13. Note: The values we obtained are shown on printout. The intercept is labeled “constant.” Slope is labeled by name of predictor variable.

14. Making a Prediction Suppose that we want to predict Darts score for a new Dow Score of 200 We predict that Darts will be at 23.65 when Dow is at 25 Check with data: what is real value of Darts when Dow is 25

15. Residual Prediction

16. Errors of Prediction Residual variance X XThe variability of predicted values Standard error of estimate X XThe standard deviation of predicted values

17. Standard Error of Estimate A common measure of the accuracy of our predictions X XWe want it to be as small as possible.

18. r 2 as % Predictable Variability Define Sum of SquaresDefine Sum of Squares

19. Major Points Predicting one dependent variable from multiple predictor variables Example with Product Advisor Data Multiple correlation Regression equation Predictions

20. The Problem In the product advisor study, we asked participants to rate the system on a number of aspects: e.g, usefulness, ease of use, trust, kind of product information, number of ratings etc. Lets think of overall usefulness as our dependent variable. Which of the above factors can predict overall usefulness? What percentage variance do they explain in the usefulness overall? What factors play the more important role?

21. Product Advisor Data

22. Correlational Matrix

23. Regression Results (using simple linear regression using method “enter” R 2, Percentage of Variance

24. Regression is significant Importance of each variable Is contribution significant?

25. Regression Coefficients Slopes and an intercept.Slopes and an intercept. Each variable adjusted for all others in the model.Each variable adjusted for all others in the model. Just an extension of slope and intercept in simple regressionJust an extension of slope and intercept in simple regression SPSS output on next slideSPSS output on next slide

26. Regression Equation A separate coefficient for each variable X XThese are slopes An intercept (here called b 0 instead of a)