Business Forecasting / Edition 6 available in Hardcover
- Pub. Date:
- Prentice Hall Professional Technical Reference
|Publisher:||Prentice Hall Professional Technical Reference|
|Edition description:||Older Edition|
|Product dimensions:||7.81(w) x 9.59(h) x 1.08(d)|
About the Author
John E. Hanke
Eastern Washington University, Emeritus
Dean W. Wichern
Texas A&M University
In the first eight editions, the computer was recognized as a powerful tool in forecasting.
The computer is even more important now with the availability of powerful
forecasting software and easy access to data via networking capabilities and the
A nationwide research study of all AACSB member institutions conducted by
the authors to determine what faculty do about using computers for teaching forecasting
showed that (1) most forecasting faculty (94.2%) attempt to provide
students with hands-on experience in using the computer, and (2) several statistical
packages and specific personal computer forecasting packages were mentioned in
the survey. The packages mentioned most frequently were Minitab, SAS, and
The authors have tried several different approaches to help faculty and students
use the computer for forecasting.This edition features the following:
1. Minitab instructions presented at the end of most chapters.
2. Excel instructions presented at the end of most chapters.
3. Three data collections available on the Internet (Minitab, Excel, other programs).
Each collection contains data from the text examples and problems. Each collection
also contains several new data series.To access the data sets on the Internet go
to the Prentice Hall Web site at www.prenhall.com/hanke
4. Examples of different computer outputs are placed throughout the text.
Table of Contents1. Introduction to Forecasting.
2. A Review of Basic Statistical Concepts.
3. Data Sources.
4. Exploring Data Patterns and Choosing a Forecasting Technique.
5. Moving Averages and Smoothing Methods.
6. Regression Analysis.
7. Multiple Regression.
8. Time Series Analysis.
9. Regression of Time Series Data.
10. The Box-Jenkins (ARIMA) Methodology.
11. Judgmental Elements in Forecasting.
Appendix A. Derivations.
Appendix B. Data for Case Study 7.1.
Appendix C. Tables.
Appendix D. Data Sets and Database.