1. Guide to Using Minitab For Basic Statistical Applications To Accompany Business Statistics: A Decision Making Approach, 6th Ed. Chapter 15: Analyzing and Forecasting Time Series Data By Groebner, Shannon, Fry, & Smith Prentice-Hall Publishing Company Copyright, 2005

2. Chapter 15 Minitab Examples  Trend Based Forecasting Trend Based Forecasting Taft Ice Cream Company  Nonlinear Trend Nonlinear Trend Harrison Equipment Company  Seasonal Adjustment Seasonal Adjustment Big Mountain Ski Resort  Single Exponential Smoothing Single Exponential Smoothing Humboldt Electronics Company More Examples

3. Chapter 15 Minitab Examples  Double Exponential Smoothing Double Exponential Smoothing Billingsley Insurance Company

4. Trend Based Forecasting - Taft Ice Cream Company Issue: The owners of Taft Ice Cream Company considering expanding their manufacturing facilities. The bank requires a forecast of future sales. Objective: Use Minitab to build a forecasting model based on 10 years of data. Data file is Taft.mtw

5. Open File Taft.mtw Trend Based Forecasting – Taft Ice Cream Company

6. First click on Graph, then Time Series Plot. Trend Based Forecasting – Taft Ice Cream Company

7. In Y, enter data column. Under Time Scale choose Calendar and time unit. Click on Options. Trend Based Forecasting – Taft Ice Cream Company

8. Enter 1991 as the starting year. Click OK. Trend Based Forecasting – Taft Ice Cream Company

9. The linear trend is evident in this time series plot. Trend Based Forecasting – Taft Ice Cream Company

10. First click on Stat, then Time Series and finally Trend Analysis. Trend Based Forecasting – Taft Ice Cream Company

11. Identify the columns containing the Variable forming the time series. Also specify a Linear Model. Trend Based Forecasting – Taft Ice Cream Company

12. Minitab constructs the graph, shows the Linear Trend Model and the values of MAD and MSD. Trend Based Forecasting – Taft Ice Cream Company

13. Issue: Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors. Harrison Equipment is interested in forecasting future repair costs for a crawler tractor it leases to contractors.Objective: Use Minitab to develop a nonlinear forecasting model. Data file is Harrison.mtw Nonlinear Trend – Harrison Equipment Company Nonlinear Trend – Harrison Equipment Company

14. Open File Harrison.mtw Nonlinear Trend – Harrison Equipment Company

15. First click on Stat, then Time Series and finally Trend Analysis. Nonlinear Trend – Harrison Equipment Company

16. Identify the time series Variable and the Model Type. Nonlinear Trend – Harrison Equipment Company

17. The Minitab output shows a plot of the data and trend line, the Linear Model and the MAPE, MAD and MSD values. Nonlinear Trend – Harrison Equipment Company

18. To develop a nonlinear model, click on Stat, then Time Series and finally Trend Analysis. Nonlinear Trend – Harrison Equipment Company

19. This time specify a Quadratic Model. Nonlinear Trend – Harrison Equipment Company

20. The Minitab output shows a plot of the data and trend line, the Quadratic Model and the MAPE, MAD and MSD values. Nonlinear Trend – Harrison Equipment Company

21. Click on Calc, then Calculator. Nonlinear Trend – Harrison Equipment Company

22. Identify column for new variable, in Expressions box enter form of new variable. Click OK Nonlinear Trend – Harrison Equipment Company

23. Click on Stat, then Regression and Regression again. Nonlinear Trend – Harrison Equipment Company

24. Define the Response variable (repair Costs) Predictors (Qtr2) then click Storage. Nonlinear Trend – Harrison Equipment Company

25. Under Diagnostic Measures select Residuals, under Characterist ics select Fits. Click OK twice. Nonlinear Trend – Harrison Equipment Company

26. The Minitab output shows the regression model. It will also give a histogram plot of the residuals. Nonlinear Trend – Harrison Equipment Company

27. Seasonal Adjustment - Big Mountain Ski Resort Seasonal Adjustment - Big Mountain Ski Resort Issue: The resort wants to build a forecasting model from data that has a definite seasonal component. Objective: Use Minitab to develop a forecasting model adjusting for seasonal data. Data file is Big Mountain.mtw

28. Open File Big Mountain.mtw Seasonal Adjustment – Big Mountain Ski Resort

29. Click on Stat, then Time Series and then select Decomposition. Seasonal Adjustment – Big Mountain Ski Resort

30. Define the Variable, the Type of Model, the Periods in a season and the Model Components. Seasonal Adjustment – Big Mountain Ski Resort

31. The graph shows the actual and predicted values. Seasonal Adjustment – Big Mountain Ski Resort

32. This graph shows the original data and other graphs. Seasonal Adjustment – Big Mountain Ski Resort

33. The Trend Line Equation, the Seasonal Indices and MAPE, MAD and MSD are also given. Seasonal Adjustment – Big Mountain Ski Resort

34. Single Exponential Smoothing Humboldt Electronics Issue: The company needs to develop a forecasting model to help make inventory decisions, and wants the model to give more weight to recent values than to regression model do. Objective: Use Minitab to develop a single exponential smoothing forecasting model. Data file is Humboldt.mtw

35. Open File Humboldt.mtw Single Exponential Smoothing – Humboldt Electronics

36. Click on Stat, then Time Series and finally Single Exponential Smoothing. Single Exponential Smoothing – Humboldt Electronics

37. Identify the Time Series Variable. Either specify alpha or ask Minitab to optimize the forecasting model. Single Exponential Smoothing – Humboldt Electronics

38. The graph shows the actual and forecast values. The accuracy measures are also given. Single Exponential Smoothing – Humboldt Electronics

39. Identify the Time Series Variable. Either specify alpha or ask Minitab to optimize the forecasting model. Single Exponential Smoothing – Humboldt Electronics

40. The graph shows the actual and forecast values. The accuracy measures and the optimum alpha are also given. Single Exponential Smoothing – Humboldt Electronics

41. Issue: The claims manager has data for 12 months and wants to forecast claims for month 13. But the time series contains a strong upward trend Objective: Use Minitab to develop a double exponential smoothing model. Data file is Billingsley.mtw Use Minitab to develop a double exponential smoothing model. Data file is Billingsley.mtw Double Exponential Smoothing Billingsley Insurance

42. Open file Billingsley.mtw Double Exponential Smoothing – Billingsley Insurance

43. Click on Stat then Time Series and finally Double Exponential Smoothing. Double Exponential Smoothing – Billingsley Insurance

44. Identify the Time Series Variable. Either specify alpha and beta or ask Minitab to optimize the forecasting model. Double Exponential Smoothing – Billingsley Insurance

45. The graph shows the actual and forecast values. The accuracy measures are also given. Double Exponential Smoothing – Billingsley Insurance

46. Identify the Time Series Variable. Either specify alpha and beta or ask Minitab to optimize the forecasting model. Double Exponential Smoothing – Billingsley Insurance

47. The graph shows the actual and forecast values. The accuracy measures and the optimum Alpha and Beta are also given. Double Exponential Smoothing – Billingsley Insurance