Johannes Ledolter – författare

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: ''Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.'' It does just that!" -Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates." -Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

Take a look at the cover of this book, particularly the two images you see there. One image is of electronic components, which makes sense-quality management has been an important tool in the electronics industry for some time now. The other image however is a little more surprising. What does a short-order cook have to do with quality? The answer is simple-quality is needed everywhere, from high-tech manufacturing to pancakes. In fact, quality management is vital to the production of just about any goods, services, or information products you can imagine. This book is all about quality-how to achieve it, how to measure it, how to maintain it. In it, you'll explore all aspects of quality, from quality of goods and services to the processes that are necessary to achieve quality. Plus, this book will show you quality management in action. A series of real-life cases illustrate how theory hits the workplace, including:* The Pancake Dilemma, in which you are asked to determine a pancake quality improvement plan* Acme Electronics, in which you are asked to critique their new quality programs, and list some specific steps to take to improve them.

The text is written for technique oriented courses on quality like Introduction to Statistical Quality Control/Process Improvement that stress the use of statistical tools.In addition to the statistical tools, the text contains a section (Chapters 2-5) on non-statistical problem solving tools for quality improvement.The main audience for this book are students in both undergraduate and graduate-level courses on Quality or Quality Control taught in Industrial Engineering programs, Statistics Departments and Schools of Business and Management.Other courses include: Applied Statistical Methods, Engineering Statistics, and Statistics for MBA or Executive MBA programs.This book is also appropriate for use in in-house company training courses on quality.

The Wiley-Interscience Paperback Series consists of selected books that have been made more accessible to consumers in an effort to increase global appeal and general circulation. With these new unabridged softcover volumes, Wiley hopes to extend the lives of these works by making them available to future generations of statisticians, mathematicians, and scientists. "This book, it must be said, lives up to the words on its advertising cover: 'Bridging the gap between introductory, descriptive approaches and highly advanced theoretical treatises, it provides a practical, intermediate level discussion of a variety of forecasting tools, and explains how they relate to one another, both in theory and practice.' It does just that!"-Journal of the Royal Statistical Society "A well-written work that deals with statistical methods and models that can be used to produce short-term forecasts, this book has wide-ranging applications. It could be used in the context of a study of regression, forecasting, and time series analysis by PhD students; or to support a concentration in quantitative methods for MBA students; or as a work in applied statistics for advanced undergraduates."-Choice Statistical Methods for Forecasting is a comprehensive, readable treatment of statistical methods and models used to produce short-term forecasts. The interconnections between the forecasting models and methods are thoroughly explained, and the gap between theory and practice is successfully bridged. Special topics are discussed, such as transfer function modeling; Kalman filtering; state space models; Bayesian forecasting; and methods for forecast evaluation, comparison, and control. The book provides time series, autocorrelation, and partial autocorrelation plots, as well as examples and exercises using real data. Statistical Methods for Forecasting serves as an outstanding textbook for advanced undergraduate and graduate courses in statistics, business, engineering, and the social sciences, as well as a working reference for professionals in business, industry, and government.

This book is about the power of statistical experiments. In the past, books on experimental design focused almost entirely on manufacturing problems. In contrast, although this book is relevant to manufacturing and includes useful examples in that area, its emphasis is on applications to marketing and service operations. The authors provide a fresh and practical treatment of the key topics in designing and analyzing experiments. Testing in the business world is commonplace, and the usual approach is to change one factor at a time while holding other factors constant. This approach may seem logical and appealing, but, as the authors explain, it is highly inefficient, and may lead to wrong conclusions. The better method, the authors propose, is to test all factors simultaneously. Doing so not only reduces the costs of experimenting but also provides the decision-maker with better information.Throughout the book, the authors illustrate concepts with practical examples. In addition, the book includes a set of 13 separate real-world cases based on the actual implementation of experimental design methods.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents: A thorough discussion and extensive demonstration of the theory behind the most useful data mining toolsIllustrations of how to use the outlined concepts in real-world situationsReadily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materialsNumerous exercises to help readers with computing skills and deepen their understanding of the materialData Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.

Collecting, analyzing, and extracting valuable information from a large amount of data requires easily accessible, robust, computational and analytical tools. Data Mining and Business Analytics with R utilizes the open source software R for the analysis, exploration, and simplification of large high-dimensional data sets. As a result, readers are provided with the needed guidance to model and interpret complicated data and become adept at building powerful models for prediction and classification.

Highlighting both underlying concepts and practical computational skills, Data Mining and Business Analytics with R begins with coverage of standard linear regression and the importance of parsimony in statistical modeling. The book includes important topics such as penalty-based variable selection (LASSO); logistic regression; regression and classification trees; clustering; principal components and partial least squares; and the analysis of text and network data. In addition, the book presents:

A thorough discussion and extensive demonstration of the theory behind the most useful data mining tools Illustrations of how to use the outlined concepts in real-world situations Readily available additional data sets and related R code allowing readers to apply their own analyses to the discussed materials Numerous exercises to help readers with computing skills and deepen their understanding of the material

Data Mining and Business Analytics with R is an excellent graduate-level textbook for courses on data mining and business analytics. The book is also a valuable reference for practitioners who collect and analyze data in the fields of finance, operations management, marketing, and the information sciences.