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Download Forecasting and Time Series: An Applied Approach (The Duxbury Advanced Series in Statistics and Decision Sciences) fb2

by Richard T. O'Connell,Bruce L. Bowerman

  • ISBN: 0534932517
  • Category: Math & Science
  • Author: Richard T. O'Connell,Bruce L. Bowerman
  • Subcategory: Mathematics
  • Other formats: mobi doc azw lit
  • Language: English
  • Publisher: South-Western College Pub; 3 edition (January 7, 1993)
  • Pages: 726 pages
  • FB2 size: 1818 kb
  • EPUB size: 1807 kb
  • Rating: 4.1
  • Votes: 228
Download Forecasting and Time Series:  An Applied Approach (The Duxbury Advanced Series in Statistics and Decision Sciences) fb2

Bruce Bowerman, Richard O'Connell, and Anne . Professor Koehler began teaching statistics in 1975 and forecasting in 1990.

Bruce Bowerman, Richard O'Connell, and Anne Koehler clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management. Richard T. O¿Connell is an associate professor of decision sciences at Miami University in Oxford, Ohio. She teaches courses in basic statistics, regression analysis, time series forecasting, and survey sampling.

Forecasting and Time Series: An Applied Approach (The Duxbury Advanced Series . 5th ed. Florence, KY: South-Western College Publishing, 2007. Time Series Analysis: Univariate and Multivariate Methods. 2nd ed. New York: Pearson, 2006.

Forecasting and Time Series: An Applied Approach (The Duxbury Advanced Series in Statistics and Decision Sciences). Belmont, CA: Duxbury Press, 1993. Sr. Forecasting Principles and Applications. Boston: Irwin/McGraw-Hill, 1998. Business Forecasting. 9th ed. Upper Saddle River:, NJ: Prentice Hall, 2008. Forecasting Methods and Applications.

Books, images, historic newspapers, maps, archives and more. Time-series analysis. Análisis de series de tiempo. Collapse Availability. Accompanying CD-ROM contains datasets in the floowing formats: ASCII, EXCEL, SAS, JMP, MINITAB, STATA, S-PLUS, EVIEWS.

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Story time just got better with Prime Book Box, a subscription that delivers hand-picked children’s books every 1, 2, or 3 months. This text is designed for practitioners and students of applied statistical forecasting. It is suitable for both the undergraduate or graduate business student. The text presents structured, detailed discussions of the concepts, and step-by-step procedures for using current forecasting methods. Series: The Duxbury series in statistics and decision sciences. Hardcover: 573 pages. Publisher: Pws Pub Co (January 1, 1989).

Introduction to Regression, Time Series, and Forecasting. A. H. M. Rahmatullah Imon. By the crisp-input and fuzzy-output fuzzy grey model GM(1,1)model, a decision making can obtain more information from the obtained possible forecasting interval and so reduce the possible loss in decision making under uncertainty with limited data. Finally, an example is given for illustration.

Gaynor and Kirkpatrick, 1994: Introduction to Time-Series Modeling and Forecasting in Business and Economics, McGraw-Hill, Inc. ISBN: 0-07-034913-4. Pankratz, 1994: Forecasting with Dynamic Regression Models, Wiley-Interscience.

Bruce L. Bowerman, Richard T. O'Connell. Forecasting and Time Series - An Applied Approach. Transforming a Seasonal Time Series into a Stationary Time Series. Three Examples of Seasonal Modeling and Forecasting. Box-Jenkins Error Term Models in Time Series Regression. 12. Advanced Box-Jenkins Modeling. The General Seasonal Model and Guidelines for Tentative Identification. Bowerman and Richard T. O'Connell clearly demonstrate the necessity of using forecasts to make intelligent decisions in marketing, finance, personnel management, production scheduling, process control, and strategic management.

Forecasting and Time Series book.

This text introduces readers to time series and forecasting techniques and contains coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting

This text introduces readers to time series and forecasting techniques and contains coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting. A chapter on basic statistical concepts and nearly 400 new computer printouts of Minitab and SAS have been added. Extensive use of Minitab and SAS output, including end-of-chapter sections explaining the use of these packages, gives students experience using forecasting software.

This comprehensive book introduces students to time series and forecasting techniques. The prerequisites are college algebra and basic statistics. It contains complete coverage of linear regression analysis, which provides much of the conceptual foundation of forecasting.
Reviews about Forecasting and Time Series: An Applied Approach (The Duxbury Advanced Series in Statistics and Decision Sciences) (3):
Gajurus
This is a very well written textbook on time series forecasting. This 725 page textbook provides thorough coverage of time series methods from elementary statistics to Box-Jenkins models and transfer functions and intervention models. It is easy to read and includes many tables of actual data which are analyzed. Highly recommended.
Tholmeena
I reviewed the third edition of this book for the American Statistician in 1994. The book covers most of the important topics for an applied course and has a reasonable list of references. There are many examples and homework exercises. Statistical software packages such as SAS and MINITAB are used throughout in example problems. The early chapters cover the basics of statistical inference and regression (Chapters 2-5). This material can be skipped in a first time series course if introductory statistics is a prerequisite.

The latter chapters cover time series regression, seasonal decomposition methods, exponential smoothing and Box-Jenkins methods. But this book does not include nonlinear time series models and it overlooks the recent and popular state space approach to time series modeling. Multivariate time series methods are also left out, though perhaps they are more appropriate for an advanced or second course in time series analysis.

The cookbook nature of the text can be found in the guidelines given for Box-Jenkins model identification. The statistical theory that the methods rely on is avoided. Although a number of important probability distributions are used with their relevant statistical tables, the underlying assumptions and distributional theory is completely avoided.

Important concepts such as the central limit theorem and the concept of a stationary stochastic process are given only very brief treatment. Other concepts are oversimplified to avoid the need for the development of any distribution theory.

This book will serve well for a course in which the student is interested in how to implement exponential smoothing and the general class of Box-Jenkins models through the use of standard statistical packages. However if the instructor wants depth of understanding the text is not adequate. Frequecy domain methods often useful in engineering applications are not even discussed.

While the book covers forecasting applications, it does not consider applications to decomposition of variance or discriminant analysis. Time series methods are also applicable in these contexts. Abraham and Ledolter (1984) "Statistical Methods for Forecasting" cover the same topics but in much greater depth. Also Janacek and Swift (1993) "Time Series: Forecasting, Simulation, Applications" is slightly more advanced and provides broader coverage. Anyone interested in the theory can consult a number of good books including the latest edition of Brockwell and Davis "Time Series: Theory and Methods". Shumway and Stoffer (2000) "Time Series Analysis and Its Applications" is up-to-date, comprehensive and has many good engineering applications.
Arlana
This book covers step by step methodology and theory for the basic time series concepts. It has worked out examples with even the most rudimentary calculations demonstrated for complex subjects like ARMA and Box-Cox decomposition. It is a good book for basic practitioners and those with a basic interest in time series analysis

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