Douglas C. Montgomery
Solutions Manual to Accompany Introduction to Time Series Analysis and Forecasting
Buch
Introduction to Time Series Analysis and Forecasting presents the time series analysis branch of applied statistics as the underlying methodology for developing practical forecasts. It bridges the gap between theory and practice by equipping readers with the tools needed to analyze time-oriented data and construct useful, short- to medium-term, statistically based forecasts.
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Produktdetails
Weitere Autoren: Jennings, Cheryl L. / Kulahci, Murat
- ISBN: 978-0-470-43574-8
- EAN: 9780470435748
- Produktnummer: 4052078
- Verlag: John Wiley & Sons
- Sprache: Englisch
- Erscheinungsjahr: 2009
- Seitenangabe: 88 S.
- Masse: H23.4 cm x B15.6 cm x D0.5 cm 149 g
- Abbildungen: Paperback
- Gewicht: 149
Über den Autor
Douglas C. Montgomery, PhD, is Regents' Professor of Industrial Engineering and Statistics at Arizona State University. Dr. Montgomery has over thirty years of academic and consulting experience and has devoted his research to engineering statistics, specifically the design and analysis of experiments, statistical methods for process monitoring and optimization, and the analysis of time-oriented data. He has authored or coauthored over 190 journal articles and eleven books, including Introduction to Linear Regression Analysis, Fourth Edition and Generalized Linear Models: With Applications in Engineering and the Sciences, both published by Wiley.Cheryl L. Jennings, PhD, is a Process Design Consultant with Bank of America. An active member of both the American Statistical Association and the American Society for Quality, her areas of research and professional interest include Six Sigma; modeling and analysis; and process control and improvement. Dr. Jennings earned her PhD in industrial engineering from Arizona State University.Murat Kulahci, PhD, is Associate Professor in Informatics and Mathematical Modelling at the Technical University of Denmark. He has authored or coauthored over thirty journal articles in the areas of time series analysis, design of experiments, and statistical process control and monitoring.
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