Steven E Rigdon - Böcker

With COVID-19 sweeping across the globe with near impunity, it is thwarting governments and health organizations efforts to contain it. Not since the 1918 Spanish Flu have citizens of developed countries experienced such a large-scale disease outbreak that is having devastating health and economic impacts. One reason such outbreaks are not more common has been the success of the public health community, including epidemiologists and biostatisticians, in identifying and then mitigating or eliminating the outbreaks. Monitoring the Health of Populations by Tracking Disease Outbreaks: Saving Humanity from the Next Plague is the story of the application of statistics for disease detection and tracking. The work of public health officials often crucially depends on statistical methods to help discern whether an outbreak may be occurring and, if there is sufficient evidence of an outbreak, then to locate and track it. Statisticians also help collect critical information, and they analyze the resulting data to help investigators zero in on a cause for a disease. With the recent outbreaks of diseases such as swine and bird flu, Ebola, and now COVID-19, the role that epidemiologists and biostatisticians play is more important than ever.Features:· Discusses the crucial roles of statistics in early disease detection.· Outlines the concepts and methods of disease surveillance.· Covers surveillance techniques for communicable diseases like Zika and chronic diseases such as cancer.· Gives real world examples of disease investigations including smallpox, syphilis, anthrax, yellow fever, and microcephaly (and its relationship to the Zika virus).Via the process of identifying an outbreak, finding its cause, and developing a plan to prevent its reoccurrence, this book tells the story of how medical and public health professionals use statistics to help mitigate the effects of disease. This book will help readers understand how statisticians and epidemiologists help combat the spread of such diseases in order to improve public health across the world.

A unique, practical guide for industry professionals who need to improve product quality and reliability in repairable systems Owing to its vital role in product quality, reliability has been intensely studied in recent decades. Most of this research, however, addresses systems that are nonrepairable and therefore discarded upon failure. Statistical Methods for the Reliability of Repairable Systems fills the gap in the field, focusing exclusively on an important yet long-neglected area of reliability. Written by two highly recognized members of the reliability and statistics community, this new work offers a unique, systematic treatment of probabilistic models used for repairable systems as well as the statistical methods for analyzing data generated from them. Liberally supplemented with examples as well as exercises boasting real data, the book clearly explains the difference between repairable and nonrepairable systems and helps readers develop an understanding of stochastic point processes. Data analysis methods are discussed for both single and multiple systems and include graphical methods, point estimation, interval estimation, hypothesis tests, goodness-of-fit tests, and reliability prediction. Complete with extensive graphs, tables, and references, Statistical Methods for the Reliability of Repairable Systems is an excellent working resource for industry professionals involved in producing reliable systems and a handy reference for practitioners and researchers in the field.

Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.

Introduction to Probability and Statistics for Data Science provides a solid course in the fundamental concepts, methods and theory of statistics for students in statistics, data science, biostatistics, engineering, and physical science programs. It teaches students to understand, use, and build on modern statistical techniques for complex problems. The authors develop the methods from both an intuitive and mathematical angle, illustrating with simple examples how and why the methods work. More complicated examples, many of which incorporate data and code in R, show how the method is used in practice. Through this guidance, students get the big picture about how statistics works and can be applied. This text covers more modern topics such as regression trees, large scale hypothesis testing, bootstrapping, MCMC, time series, and fewer theoretical topics like the Cramer-Rao lower bound and the Rao-Blackwell theorem. It features more than 250 high-quality figures, 180 of which involve actual data. Data and R are code available on our website so that students can reproduce the examples and do hands-on exercises.

ENABLES READERS TO UNDERSTAND THE METHODS OF EXPERIMENTAL DESIGN TO SUCCESSFULLY CONDUCT LIFE TESTING TO IMPROVE PRODUCT RELIABILITY This book illustrates how experimental design and life testing can be used to understand product reliability in order to enable reliability improvements. The book is divided into four sections. The first section focuses on statistical distributions and methods for modeling reliability data. The second section provides an overview of design of experiments including response surface methodology and optimal designs. The third section describes regression models for reliability analysis focused on lifetime data. This section provides the methods for how data collected in a designed experiment can be properly analyzed. The final section of the book pulls together all of the prior sections with customized experiments that are uniquely suited for reliability testing. Throughout the text, there is a focus on reliability applications and methods. It addresses both optimal and robust design with censored data. To aid in reader comprehension, examples and case studies are included throughout the text to illustrate the key factors in designing experiments and emphasize how experiments involving life testing are inherently different. The book provides numerous state-of-the-art exercises and solutions to help readers better understand the real-world applications of experimental design and reliability. The authors utilize R and JMP® software throughout as appropriate, and a supplemental website contains the related data sets. Written by internationally known experts in the fields of experimental design methodology and reliability data analysis, sample topics covered in the book include: An introduction to reliability, lifetime distributions, censoring, and inference for parameter of lifetime distributionsDesign of experiments, optimal design, and robust designLifetime regression, parametric regression models, and the Cox Proportional Hazard ModelDesign strategies for reliability achievementAccelerated testing, models for acceleration, and design of experiments for accelerated testingThe text features an accessible approach to reliability for readers with various levels of technical expertise. This book is a key reference for statistical researchers, reliability engineers, quality engineers, and professionals in applied statistics and engineering. It is a comprehensive textbook for upper-undergraduate and graduate-level courses in statistics and engineering.