1. Guide to Using Excel For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 6th Ed. Chapter 14: Multiple Regression Analysis and Model Building By Groebner, Shannon, Fry, & Smith Prentice-Hall Publishing Company Copyright, 2005

2. Chapter 14 Excel Examples  Multiple Regression Multiple Regression First City Real Estate  Multiple Regression –Dummy Variable Multiple Regression –Dummy Variable First City Real Estate  Curvilinear Regression Curvilinear Regression Ashley Investment Services  Interaction Effects Interaction Effects Ashley Investment Services More Examples

3. Chapter 14 Excel Examples  Residual Analysis Residual Analysis First City Real Estate

4. Multiple Regression – First City Real Estate Issue: First City management wishes to build a model that can be used to predict sales prices for residential property. Objective: Use Excel to build a multiple regression model relating sales price to a set of measurable variables. Data file is First City.xls

5. Open File First City.xls Multiple Regression – First City Real Estate

6. First click on Tools, then Data Analysis and finally on Correlation. Multiple Regression – First City Real Estate

7. Define ranges for Variables. Click on O.K. Multiple Regression – First City Real Estate

8. The Excel output shows the correlation between Age and Square Feet. Multiple Regression – First City Real Estate

9. Return to the data sheet, click on Tools, Data Analysis and then Regression. Multiple Regression – First City Real Estate

10. Identify range of dependent and independent variables. Click OK. Multiple Regression – First City Real Estate

11. Excel determines the regression model. Multiple Regression – First City Real Estate

12. To determine the Variance Inflation Factors, return to the data sheet, click on PHStat, Regression and Multiple Regression. Multiple Regression – First City Real Estate

13. Identify range of dependent and independent variables. Click on VIF then click OK.

14. Multiple Regression – First City Real Estate PHStat will find Variance Inflation Factors for all independent variables.

15. Issue: First City managers wish to improve the model by adding a location variable. First City managers wish to improve the model by adding a location variable.Objective: Use Excel to improve a regression model by adding a dummy variable. Data file is First City.xls Multiple Regression - Dummy Variable -First City Real Estate

16. Open File First City.xls click on the worksheet containing the Area data. Multiple Regression- Dummy Variable – First City Real Estate

17. Click on PHStat then Regression and Multiple Regression. Multiple Regression- Dummy Variable – First City Real Estate

18. Define the Data ranges for the Y and X Variable then select Regression Statistics Table, ANOVA Coefficients Table and VIF. Multiple Regression- Dummy Variable – First City Real Estate

19. The output shows improved Regression Statistics. Multiple Regression- Dummy Variable – First City Real Estate

20. Curvilinear Relationships - Ashley Investment Services Issue: The director of personnel is trying to determine whether there is a relationship between employee burnout and time spent socializing with co-workers. Objective: Use Excel to determine whether the relationship between the two measures is statistically significant. Data file is Ashley.xls

21. Open File Ashley.xls File contains values for 20 Investment Advisors. Curvilinear Relationships – Ashley Investment Services

22. To develop the scatter plot first click on Chart Wizard button then select XY (Scatter). Curvilinear Relationships – Ashley Investment Services

23. Define the Data range and remember the X variable must be in the left column. You may have to reverse the columns by cutting and pasting. Curvilinear Relationships – Ashley Investment Services

24. Label the X and Y axis. Curvilinear Relationships – Ashley Investment Services

25. Find the scatter plot. Curvilinear Relationships – Ashley Investment Services

26. To find the regression model, return to the data sheet, click on Tools, then Data Analysis and finally Regression. Curvilinear Relationships – Ashley Investment Services

27. Identify the range for the X and Y variables. Then click OK. Curvilinear Relationships – Ashley Investment Services

28. The output shows the R Square value and the Regression Coefficients. Curvilinear Relationships – Ashley Investment Services

29. To develop a nonlinear model, return to the data file. Create a new variable Socialization Squared. Curvilinear Relationships – Ashley Investment Services

30. To find the regression model, click on Tools, then Data Analysis and finally Regression. Curvilinear Relationships – Ashley Investment Services

31. Identify the range for the X and Y variables. Then click OK. Curvilinear Relationships – Ashley Investment Services

32. The output shows the R Square value and the Regression Coefficients. Curvilinear Relationships – Ashley Investment Services

33. For an alternate way to find a nonlinear model, return to the scatter plot, left click on any point on the graph then right click. Select Add Trendline. Curvilinear Relationships – Ashley Investment Services

34. Select Exponential. Curvilinear Relationships – Ashley Investment Services

35. This gives the Exponential Regression Line. To add the Regression Equation, left click on the trend line, then right click to Format Trendline Curvilinear Relationships – Ashley Investment Services

36. Select Options. Check Display equation and Display R- square. Curvilinear Relationships – Ashley Investment Services

37. This adds the Regression Equation and R- square value to the Trend Line. Curvilinear Relationships – Ashley Investment Services

38. Interaction Effects - Ashley Investment Services Interaction Effects - Ashley Investment Services Issue: The director of personnel is trying to determine whether the model can be improved by separating observations between those taken from men and women. Objective: Use Excel to determine whether the relationship between the measures can be improved. Data file is Ashley-2.xls

39. Open File Ashley-2.xls Curvilinear Relationships –Ashley Investment Services

40. Group the data from males and females together by identifying the range of data, then clicking on Data then Sort. Curvilinear Relationships –Ashley Investment Services

41. Click on the Chart Wizard and then (XY) Scatter. Click Next Curvilinear Relationships –Ashley Investment Services

42. Click on the Series Tab. Identify the range for data from Males. Curvilinear Relationships –Ashley Investment Services

43. Add Females to the Series and identify the appropriate Data Range. Curvilinear Relationships –Ashley Investment Services

44. Label the Chart and the X and Y Axes. Then click Next. Curvilinear Relationships –Ashley Investment Services

45. The scatter plot now separates male and female data points. Curvilinear Relationships –Ashley Investment Services

46. To find a nonlinear model for each data group, left click on any point on each series then right click. Select Add Trendline then Exponential. Curvilinear Relationships –Ashley Investment Services

47. Add colors by left cicking on each Trendline, then right click and select Format Trendline. Curvilinear Relationships –Ashley Investment Services

48. A nonlinear model can also be developed by creating new variables and proceeding as in the last tutorial. Curvilinear Relationships – Ashley Investment Services

49. To find the regression model, click on Tools, then Data Analysis and finally Regression. Curvilinear Relationships – Ashley Investment Services

50. Identify the range for the X and Y variables. Then click OK. Curvilinear Relationships – Ashley Investment Services

51. The output shows the R Square value and the Regression Coefficients. Curvilinear Relationships – Ashley Investment Services

52. Issue: The company is interested in analyzing the residuals of the regression model to determine whether the assumptions are satisfied. Objective: Use Excel to analyze residuals from a regression model. Data file is First City-3.xls Use Excel to analyze residuals from a regression model. Data file is First City-3.xls Residual Analysis - First City Real Estate Residual Analysis - First City Real Estate

53. Open file First City-3.xls Residual Analysis – First City Real Estate

54. Since Excel requires the independent variables to be adjacent, cut and paste the appropriate columns. Residual Analysis – First City Real Estate

55. First click on Tools, then Data Analysis and then on Regression. Residual Analysis – First City Real Estate

56. Define range for X and Y Variables. Excel gives several options for Residual Analysis, but does not have as complete a set as Minitab. Residual Analysis – First City Real Estate

57. Selecting the Residual Plot option on the previous page will give a set of residual plots. Residual Analysis – First City Real Estate

58. Any of the residual plots can be adjusted to give a clearer picture of the residual pattern. Residual Analysis – First City Real Estate

59. This is the output if the Normal Probability Plot option is selected. Residual Analysis – First City Real Estate

60. While Excel will not automatically generate a histogram of residual values, one can be generated. Start with the Standardized Residuals. Residual Analysis – First City Real Estate

61. Define the Bin values for the Histogram. Then click on Tools, select Data Analysis and then Histogram. Residual Analysis – First City Real Estate

62. Identify the data range for the Standard Residuals and Bins. Specify Chart Output. Residual Analysis – First City Real Estate

63. Size and format the Histogram output. Also, close the gaps between the rectangles. Residual Analysis – First City Real Estate