2. Copyright © 2011 Pearson Education, Inc. The Simple Regression Model Chapter 21

3. 21.1 The Simple Regression Model How can we test the CAPM (Capital Asset Pricing Model) for Berkshire Hathaway stock?  Formulate the simple regression with percentage change in Berkshire Hathaway stock as y and the percentage change in value of the whole stock market as x  Use inference related to regression: standard errors, confidence intervals and hypothesis tests Copyright © 2011 Pearson Education, Inc. 3 of 50

4. 21.1 The Simple Regression Model  Simple Regression Model (SRM): model for the association in the population between an explanatory variable x and response y.  Consider the data to be a sample from a population. Copyright © 2011 Pearson Education, Inc. 4 of 50

5. 21.1 The Simple Regression Model Linear on Average  The equation of the SRM describes how the conditional mean of Y depends on X.  The SRM shows that these means lie on a line with intercept β 0 and slope β 1 : Copyright © 2011 Pearson Education, Inc. 5 of 50

6. 21.1 The Simple Regression Model Deviations from the Mean  The deviations of responses around are called errors.  Error, is denoted by, and E( ) = 0. Copyright © 2011 Pearson Education, Inc. 6 of 50

7. 21.1 The Simple Regression Model Deviations from the Mean The SRM makes three assumptions about : 1. Independent. Errors are independent of each other. 2. Equal variance. All errors have the same variance, Var( ) =. 3. Normal. The errors are normally distributed. Copyright © 2011 Pearson Education, Inc. 7 of 50

8. 21.1 The Simple Regression Model Data Generating Process  Let Y denote monthly sales of a company and let X denote its spending on advertising (both in thousands of dollars).  Assume the following population model: Copyright © 2011 Pearson Education, Inc. 8 of 50

9. 21.1 The Simple Regression Model Data Generating Process The SRM assumes a normal distribution at each x. Copyright © 2011 Pearson Education, Inc. 9 of 50

10. 21.1 The Simple Regression Model Data Generating Process Eventually the data shown below are observed. Copyright © 2011 Pearson Education, Inc. 10 of 50

11. 21.1 The Simple Regression Model Data Generating Process  The true regression line is a characteristic of the population, not the observed data.  The SRM is a model and offers a simplified view of reality. Copyright © 2011 Pearson Education, Inc. 11 of 50

12. 21.1 The Simple Regression Model Simple Regression Model (SRM) Observed values of the response Y are linearly related to the values of the explanatory variable X by the equation:, ~ N(0, ). The observations are independent of one another, have equal variance around the regression line, and are normally distributed around the regression line. Copyright © 2011 Pearson Education, Inc. 12 of 50

13. 21.2 Conditions for the SRM Conditions for the SRM – Checklist  Is the association between y and x linear?  Have lurking variables been ruled out?  Are the errors evidently independent?  Are the variances of the residuals similar?  Are the residuals nearly normal? Copyright © 2011 Pearson Education, Inc. 13 of 50

14. 21.2 Conditions for the SRM Conditions for the SRM – CAPM Example Linearity condition is satisfied; no pattern in the residuals. Data are shifted to the right because of two outliers (well-known declines in the market). Copyright © 2011 Pearson Education, Inc. 14 of 50

15. 21.2 Conditions for the SRM Conditions for the SRM – CAPM Example No obvious lurking variable (according to CAPM theory). Similar variances condition is satisfied. Check the plot of residuals versus x for any fan shaped pattern (none visible). Copyright © 2011 Pearson Education, Inc. 15 of 50

16. 21.2 Conditions for the SRM Conditions for the SRM – CAPM Example Evidently independent. No dependence apparent in the timeplot of the residuals. Copyright © 2011 Pearson Education, Inc. 16 of 50

17. 21.2 Conditions for the SRM Conditions for the SRM – CAPM Example The residuals are not normally distributed. Check sample size condition (satisfied) to use CLT. Copyright © 2011 Pearson Education, Inc. 17 of 50

18. 21.2 Conditions for the SRM Modeling Process Before looking at plots, ask two questions: 1. Does a linear relationship make sense? 2. Is the relationship free of lurking variables? Then begin working with data. Copyright © 2011 Pearson Education, Inc. 18 of 50

19. 21.2 Conditions for the SRM Modeling Process  Plot y versus x and verify a linear association.  Fit the least squares line and obtain residuals.  Plot the residuals versus x.  If time series data, construct a timeplot of residuals.  Inspect the histogram and quantile plot of the residuals. Copyright © 2011 Pearson Education, Inc. 19 of 50

20. 21.3 Inference in Regression Parameters and Estimates for SRM Copyright © 2011 Pearson Education, Inc. 20 of 50

21. 21.3 Inference in Regression Standard Errors  Describe the sample-to-sample variability of b 0 and b 1  The estimated standard error of b 1 is Copyright © 2011 Pearson Education, Inc. 21 of 50

22. 21.3 Inference in Regression Estimated Standard Error of b 1 Influenced by:  Standard deviation of the residuals. As it increases, the standard error increases.  Sample size. As it increases, the standard error decreases.  Standard deviation of x. As it increases, the standard error increases. Copyright © 2011 Pearson Education, Inc. 22 of 50

23. 21.3 Inference in Regression Software Results for CAPM Example Copyright © 2011 Pearson Education, Inc. 23 of 50

24. 21.3 Inference in Regression Confidence Intervals The 95% confidence interval for β 1 is The 95% confidence interval for β 0 is Copyright © 2011 Pearson Education, Inc. 24 of 50

25. 21.3 Inference in Regression Confidence Intervals – CAPM Example The 95% confidence interval for β 1 is The 95% confidence interval for β 0 is Copyright © 2011 Pearson Education, Inc. 25 of 50

26. 21.3 Inference in Regression Hypothesis Tests To test H 0 : β 1 = 0 use To test H 0 : β 0 = 0 use Copyright © 2011 Pearson Education, Inc. 26 of 50

27. 21.3 Inference in Regression Hypothesis Tests – CAPM Example  The t-statistic of 9.29 with p-value of < 0.0001 indicates that the slope is significantly different from zero.  The t-statistic of 4.11 with p-value of < 0.0001 indicates that the intercept is significantly different from zero. Copyright © 2011 Pearson Education, Inc. 27 of 50

28. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Motivation Does traffic volume affect gasoline sales? How much more gasoline can be expected to be sold at a franchise location with an average of 40,000 drive-bys compared to one with an average of 32,000 drive-bys? Copyright © 2011 Pearson Education, Inc. 28 of 50

29. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Method Use sales data from a recent month obtained from 80 franchise outlets. The 95% confidence interval for 8,000 times the estimated slope will indicate how much more gas is expected to sell at the busier location. Copyright © 2011 Pearson Education, Inc. 29 of 50

30. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Method Association is linear; no obvious lurking variable. Copyright © 2011 Pearson Education, Inc. 30 of 50

31. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Mechanics Copyright © 2011 Pearson Education, Inc. 31 of 50

32. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Mechanics Residual plot confirms similar variances. Copyright © 2011 Pearson Education, Inc. 32 of 50

33. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Mechanics Residuals appear normally distributed. Copyright © 2011 Pearson Education, Inc. 33 of 50

34. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Mechanics The 95% confidence interval for β 1 is approximately 0.188 to 0.285 gallons/car. Hence, a difference of 8,000 cars in daily traffic volume implies a difference in average daily sales of approximately 1,507 to 2,281 more gallons per day. Copyright © 2011 Pearson Education, Inc. 34 of 50

35. 4M Example 21.1: LOCATING A FRANCHISE OUTLET Message Based on a sample of 80 gas stations, we expect that a station located at a site with 40,000 drive bys will sell on average from 1,507 to 2,281 more gallons of gas daily than a location with 32,000 drive bys. Copyright © 2011 Pearson Education, Inc. 35 of 50

36. 21.4 Prediction Intervals Leveraging the SRM  Prediction interval: an interval designed to hold a fraction (usually 95%) of the values of the response for a given value of x.  A prediction interval differs from a confidence interval because it makes a statement about the location of a new observation rather than a parameter of a population. Copyright © 2011 Pearson Education, Inc. 36 of 50

37. 21.4 Prediction Intervals Leveraging the SRM The 95% prediction interval for y new is where and Copyright © 2011 Pearson Education, Inc. 37 of 50

38. 21.4 Prediction Intervals Leveraging the SRM  A simple approximation for a 95% prediction interval is.  Prediction intervals are reliable within the range of observed data. They are also sensitive to the assumptions of constant variance and normality. Copyright © 2011 Pearson Education, Inc. 38 of 50

39. 4M Example 21.2: MANAGING NATURAL RESOURCES Motivation In managing commercial fishing fleets, the level of effort (number of boat-days) is assumed to influence the size of the catch. What is the predicted crab catch in a season with 7,500 days of effort? Copyright © 2011 Pearson Education, Inc. 39 of 50

40. 4M Example 21.2: MANAGING NATURAL RESOURCES Method Use regression with Y equal to the catch near Vancouver Island from 1980 – 2007 measured in thousands of pounds of Dungeness crabs with X equal to the level of effort (total number of days by boats catching Dungeness crabs). Copyright © 2011 Pearson Education, Inc. 40 of 50

41. 4M Example 21.2: MANAGING NATURAL RESOURCES Method Linear association is evident. Copyright © 2011 Pearson Education, Inc. 41 of 50

42. 4M Example 21.2: MANAGING NATURAL RESOURCES Mechanics Copyright © 2011 Pearson Education, Inc. 42 of 50

43. 4M Example 21.2: MANAGING NATURAL RESOURCES Mechanics Evidently independent. Copyright © 2011 Pearson Education, Inc. 43 of 50

44. 4M Example 21.2: MANAGING NATURAL RESOURCES Mechanics Similar variances confirmed. Copyright © 2011 Pearson Education, Inc. 44 of 50

45. 4M Example 21.2: MANAGING NATURAL RESOURCES Mechanics Nearly normal condition could be satisfied. Copyright © 2011 Pearson Education, Inc. 45 of 50

46. 4M Example 21.2: MANAGING NATURAL RESOURCES Mechanics The t-statistic (and p-value) indicate that the slope is significantly different from zero. The predicted catch in a year with x = 7500 days of effort is 1,173.24 thousand pounds. The 95% prediction interval is from 908.44 to 1,438.11 thousand pounds. Copyright © 2011 Pearson Education, Inc. 46 of 50

47. 4M Example 21.2: MANAGING NATURAL RESOURCES Message There is a statistically significant linear association between days of effort and total catch. On average, each additional day of effort (per boat) increases the harvest by about 160 pounds. In a season with 7,500 days of effort, there is an expected total harvest of 1,173,240 pounds. There is a 95% probability that the catch will be between 908,440 and 1,438,110 pounds. Copyright © 2011 Pearson Education, Inc. 47 of 50

48. Best Practices  Verify that your model makes sense, both visually and substantively.  Consider other possible explanatory variables.  Check the conditions, in the listed order. Copyright © 2011 Pearson Education, Inc. 48 of 50

49. Best Practices (Continued)  Use confidence intervals to express what you know about the slope and intercept.  Check the assumptions of the SRM carefully before using prediction intervals.  Be careful when extrapolating. Copyright © 2011 Pearson Education, Inc. 49 of 50

50. Pitfalls  Don’t overreact to residual plots.  Do not mistake varying amounts of data for unequal variances.  Do not confuse confidence intervals with prediction intervals.  Do not expect that r 2 and s e must improve with a larger sample. Copyright © 2011 Pearson Education, Inc. 50 of 50