ASA-SIAM Series on Statistics and Applied Probability – serie

Statisticians know that the clean data sets that appear in textbook problems have little to do with real-life industry data. To better prepare their students for all types of statistical careers, academic statisticians now strive to use data sets from real-life statistical problems. This book contains 20 case studies that use actual data sets that have not been simplified for classroom use. Each case study is a collaboration between statisticians from academe and from business, industry, or government.This book is the result of a collaborative workshop of statisticians focusing on academic?industrial partnerships. The cases come from a wide variety of application areas, including biology/environment, medical and health care, pharmaceutical, marketing and survey research, and manufacturing.

How does a marble manufacturer know that the colour will be consistent throughout the products being made? How can you tell if liquid at the bottom of a container is the same consistency as at the top? How does a pellet manufacturer know if the pellets are consistently the same size? How does a chemical manufacturer know if the percent purity in a sample is representative of the whole batch? These and similar questions are answered in this book.Statisticians are well trained in sampling techniques if the sample is well defined. Examples of such samples include industrial parts in manufacturing, invoices in business processes, and people in surveys. However, what if the sampling unit isn't well defined? What if you are sampling bulk material such as a pile of coal? Author Patricia L. Smith illustrates what to look for in sampling devices and procedures to obtain correct samples from bulk materials. She gives sampling guidelines that can be applied immediately and shows how to analyze protocols to uncover sampling problems.Smith presents the ideas of Pierre Gy in lay terms so that his concepts and principles can be easily grasped and applied. She conveys Gy's intuitive meaning while preserving his original ideas. Synonyms have been used for some technical terms to avoid confusion.Special Features:Presents Pierre Gy's approach to sampling bulk material—whether solid, liquid, or gas.Gives basic principles that can be applied to any bulk sampling situation.Uses everyday language and intuitive explanations.Gives examples of simple experiments that readers can perform on their own to understand the principles.Includes a brief summary after each chapter for easy reference.

This applied, self-contained text provides detailed coverage of the practical aspects of multivariate statistical process control (MVSPC) based on the application of Hotelling's T2 statistic. MVSPC is the application of multivariate statistical techniques to improve the quality and productivity of an industrial process. The authors, leading researchers in this area who have developed major software for this type of charting procedure, provide valuable insight into the T2 statistic. Intentionally including only a minimal amount of theory, they lead readers through the construction and monitoring phases of the T2 control statistic using numerous industrial examples taken primarily from the chemical and power industries. These examples are applied to the construction of historical data sets to serve as a point of reference for the control procedure and are also applied to the monitoring phase, where emphasis is placed on signal location and interpretation in terms of the process variables.Specifically devoted to the T2 methodology, this is the only book available that concisely and thoroughly presents such topics as how to construct a historical data set; how to check the necessary assumptions used with this procedure; how to chart the T2 statistic; how to interpret its signals; how to use the chart in the presence of autocorrelated data; and how to apply the procedure to batch processes. The book comes with a CD-ROM containing a 90-day demonstration version of the QualStat™ multivariate SPC software specifically designed for the application of T2 control procedures. The CD-ROM is compatible with Windows® 95, Windows® 98, Windows® Me Millennium Edition, and Windows NT® operating systems.

Survival data consist of a single event for each population unit, namely, end of life, which is modeled with a life distribution. In contrast, many applications involve repeated-events data, where a unit may accumulate any number of events over time. Examples include the number and cost of repairs of products, the number and treatment costs of recurrent disease episodes in patients, and the number of childbirths to statisticians. This applied book provides practitioners with basic nonparametric methods for such data, particularly the plot of the estimate of the population mean cumulative function (MCF), which yields most of the information sought.Recurrent Events Data Analysis for Product Repairs, Disease Recurrences, and Other Applications is the first book to present a simple, unified theory that includes data on costs or other ""values"" of discrete events, not just the number of events. It surveys computer programs that calculate and plot the MCF estimate with confidence limits, shows their output, and explains how to interpret such plots. Many such calculations can be easily done with a pocket calculator or spreadsheet program. Also, the book introduces basic Poisson and Cox regression models and parametric models, including homogeneous and nonhomogeneous Poisson processes and renewal processes.

The analysis of means (ANOM) is a graphical procedure used to quantify differences among treatment groups in a variety of experimental design and observational study situations. The ANOM decision chart allows one to easily draw conclusions and interpret results with respect to both statistical and practical significance. It is an excellent choice for multiple comparisons of means, rates, or proportions and can be used with both balanced and unbalanced data. Key advances in ANOM procedures that have appeared only in technical journals during the last 20 years are included in this first comprehensive modern treatment of the ANOM containing all of the needed information for practitioners to understand and apply ANOM.This book contains examples from a wide variety of fields adapted from real-world applications and data with easy-to-follow, step-by-step instructions. It is front loaded, so potential ANOM users can find solutions to standard problems in the first five chapters. An appendix contains several SAS® examples showing the system’s ANOM capabilities and how SAS was used to produce selected ANOM decision charts in the book.Given these features, the lack of any other book on ANOM, and the recent inclusion of ANOM in SAS, this book will be a welcome addition to practitioners’ and statisticians’ bookshelves, where it will serve both as a primer and reference.