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The eighth edition of Design and Analysis of Experiments continues to provide extensive and in-depth information on engineering, business, and statistics-as well as informative ways to help readers design and analyze experiments for improving the quality, efficiency and performance of working systems.
Once solely the domain of engineers, quality control has become a vital business operation used to increase productivity and secure competitive advantage. Introduction to Statistical Quality Control offers a detailed presentation of the modern statistical methods for quality control and improvement. Thorough coverage of statistical process control (SPC) demonstrates the efficacy of statistically-oriented experiments in the context of process characterization, optimization, and acceptance sampling, while examination of the implementation process provides context to real-world applications. Emphasis on Six Sigma DMAIC (Define, Measure, Analyze, Improve and Control) provides a strategic problem-solving framework that can be applied across a variety of disciplines. Adopting a balanced approach to traditional and modern methods, this text includes coverage of SQC techniques in both industrial and non-manufacturing settings, providing fundamental knowledge to students of engineering, statistics, business, and management sciences. A strong pedagogical toolset, including multiple practice problems, real-world data sets and examples, provides students with a solid base of conceptual and practical knowledge.
The text provides a practical approach oriented to engineering as well as chemical and physical sciences. Students learn how the material will be relevant in their careers through the integration throughout of unique problem sets that reflect realistic applications and situations.
Thoroughly updated throughout, Time Series Forecasting, Third Edition presents the underlying theories of times series analysis needed to analyze time-oriented data and construct real-world short-to-medium-term statistical forecasts. Authored by highly experienced academics and professionals in engineering statistics, the Third Edition features discussions on both popular and modern time series methodologies as well as introduction to Bayesian methods in forecasting. This new edition has been updated to feature new exercises and real-world examples, consolidated sections and shortened explanations to aid student comprehension, all-new videos that guide students through difficult concepts and topics, and updated ancillary resources including a solutions manual for instructors and students. Time Series Forecasting, Third Edition is an ideal textbook for upper-undergraduate and graduate-level courses in forecasting and time series. The book is also an excellent reference for practitioners and researchers who need to model and analyze time series data to generate forecasts.
INTRODUCTION TO LINEAR REGRESSION ANALYSIS
* Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.
* Montgomery, Runger, and Hubele provide modern coverage of engineering statistics, focusing on how statistical tools are integrated into the engineering problem-solving process.
Maintaining the same nontechnical approach as its acclaimed predecessor, this second edition of Generalized Linear Models is now thoroughly extended to include the latest developments in the field, the most relevant computational approaches, and the most relevant examples from the fields of engineering and physical sciences.
An accessible introduction to the most current thinking in and practicality of forecasting techniques in the context of time-oriented data. Analyzing time-oriented data and forecasting are among the most important problems that analysts face across many fields, ranging from finance and economics to production operations and the natural sciences.
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