Description - Introduction to Time Series Analysis and Forecasting by Douglas C. Montgomery
Introduction to Time Series Analysis and Forecasting examines methods for modeling and analyzing time series data with a view towards drawing inferences about the data and generating forecasts that will be useful to the decision maker. While the level is advanced undergraduate/first-year graduate, with a prerequisite knowledge of basic statistical methods, some portions of the book require a first course in calculus and modest matrix algebra manipulation skills. Minitab and SAS Software System are used extensively to illustrate how the methods in the text are implemented in practice.
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(248mm x 165mm x 28mm)
Wiley-Blackwell (an imprint of John Wiley & Sons Ltd)
Publisher: John Wiley and Sons Ltd
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Author Biography - Douglas C. Montgomery
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.