John E. Hanke


Dean W. Wichern, Texas A&M University


 

1. Introduction to Forecasting.


2. Exploring Data Patterns and Choosing a Forecasting Technique.


3. Moving Averages and Smoothing Methods.


4. Time Series and Their Components.


5. Simple Linear Regression.


6. Multiple Regression Analysis.


7. Regression with Time Series Data.


8. The Box-Jenkins (ARIMA) Methodology.


9. Judgmental Forecasting and Forecast Adjustments.


10. Managing the Forecasting Process.


Appendix A: Derivations.


Appendix B: Data for Case Study 7.1.


Appendix C: Tables.


Appendix D: Data Sets and Databases.


 

 • Emphasis on the 9th edition has been placed on the application of theoretical concepts and techniques by incorporating end-of-chapter exercises such as:


o Twelve additional cases.


o Thirty two new problem sets.


o Minitab Applications are presented at the end of each chapter with step-by-step guidance.


o Excel 2003 Applications have been added to demonstrate how a forecasting problem is solved.


• All chapters in this edition have been revised to enhance the clarity of the writing and increase teaching and learning effectiveness.  Six sections have been constructed which include:


o Background material on the nature of forecasting and a quick review of statistical concepts.


o The exploration ofdata patterns, averaging, smoothing techniques, and an introduction to time series decomposition in terms of underlying components.


o Causal forecasting techniques.


o Judgmental forecasting and managing the forecasting process.


• The authors have tried several different approaches to help faculty and students use the computer for forecasting.  This edition features the following:


o Minitab instructions.


o Excel instructions.


o Three data collections are available on the Companion Website.


o Examples of different computer outputs are place throughout the text.