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Stationary Stochastic Processes for Scientists and Engineers
by Georg Lindgren Holger Rootzen Maria SandstenSuitable for a one-semester course, this text teaches students how to use stochastic processes efficiently. Carefully balancing mathematical rigor and ease of exposition, the book provides students with a sufficient understanding of the theory and a practical appreciation of how it is used in real-life situations. Special emphasis is on the interpretation of various statistical models and concepts as well as the types of questions statistical analysis can answer. To enable hands-on practice, MATLAB code is available online.
Statistical: Ten Easy Ways to Avoid Being Misled By Numbers
by Anthony Reuben'Refreshingly clear and engaging' Tim Harford'Delightful . . . full of unique insights' Prof Sir David SpiegelhalterThere's no getting away from statistics. We encounter them every day. We are all users of statistics whether we like it or not.Do missed appointments really cost the NHS £1bn per year?What's the difference between the mean gender pay gap and the median gender pay gap?How can we work out if a claim that we use 42 billion single-use plastic straws per year in the UK is accurate?What did the Vote Leave campaign's £350m bus really mean?How can we tell if the headline 'Public pensions cost you £4,000 a year' is correct?Does snow really cost the UK economy £1bn per day?But how do we distinguish statistical fact from fiction? What can we do to decide whether a number, claim or news story is accurate? Without an understanding of data, we cannot truly understand what is going on in the world around us.Written by Anthony Reuben, the BBC's first head of statistics, Statistical is an accessible and empowering guide to challenging the numbers all around us.
Statistical Analyses for Criminal Justice and Criminology: A Conceptual Introduction
by Michael J. DeValveThis book is a how-to guide on statistical analyses designed for undergraduates and others new to the subject. It uses a conceptual framework, starting with the most basic concepts of statistics and moving up through the capacity to perform bivariate regression.Written in an easy-going and clear style, it uses policing data to illustrate concepts. Easily identified Main Take-Aways and Key Terms features aid student understanding. Designed to combat the fear of mathematics and statistics often held by students in the social sciences, plain verbiage, multiple examples, and clear demonstrations combine to achieve the actualization and proper contextualized use of univariate and bivariate statistics. This work also serves as a launching pad for further study in statistics.As an accessible introduction to statistics in criminal justice and criminology, this text will appeal to both students and instructors in introductory criminal justice and criminology statistics courses.
Statistical Analysis: The Basics (The Basics)
by Christer ThraneStatistical Analysis: The Basics provides an engaging and easy‑to‑read primer on this sometimes daunting subject. Intended for those with little or no background in mathematics or statistics, this book explores the importance of statistical analysis in the modern world by asking statistical questions about data and explains how to conduct such analyses and correctly interpret the results.Packed with everyday examples from sport, health, education, and leisure, it reinforces the understanding of core topics while avoiding the heavy use of equations and formulae. Written in a highly accessible style and adopting a hands‑on approach, each chapter is accompanied by a summary of key points, illustrations and tables, and recommendations for further reading, with the final chapter delving into the practicalities of conducting a real‑life statistical research project.Statistical Analysis: The Basics is essential reading for anyone who wishes to master the fundamentals of modern‑day statistical analysis.
Statistical Analysis and Data Display
by Richard M. Heiberger Burt HollandThis contemporary presentation of statistical methods features extensive use of graphical displays for exploring data and for displaying the analysis. The authors demonstrate how to analyze data--showing code, graphics, and accompanying tabular listings--for all the methods they cover. They emphasize how to construct and interpret graphs. They discuss principles of graphical design. They identify situations where visual impressions from graphs may need confirmation from traditional tabular results. All chapters have exercises. The authors provide and discuss R functions for all the new graphical display formats. All graphs and tabular output in the book were constructed using these functions. Complete R scripts for all examples and figures are provided for readers to use as models for their own analyses. This book can serve as a standalone text for statistics majors at the master's level and for other quantitatively oriented disciplines at the doctoral level, and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic regression, and ARIMA time series modeling. The authors illustrate classical concepts and techniques with a variety of case studies using both newer graphical tools and traditional tabular displays. The Second Edition features graphs that are completely redrawn using the more powerful graphics infrastructure provided by R's lattice package. There are new sections in several of the chapters, revised sections in all chapters and several completely new appendices. New graphical material includes: * an expanded chapter on graphics * a section on graphing Likert Scale Data to build on the importance of rating scales in fields from population studies to psychometrics * a discussion on design of graphics that will work for readers with color-deficient vision * an expanded discussion on the design of multi-panel graphics * expanded and new sections in the discrete bivariate statistics capter on the use of mosaic plots for contingency tables including the n×2×2 tables for which the Mantel-Haenszel-Cochran test is appropriate * an interactive (using the shiny package) presentation of the graphics for the normal and t-tables that is introduced early and used in many chapters The new appendices include discussions of R, the HH package designed for R (the material in the HH package was distributed as a set of standalone functions with the First Edition of this book), the R Commander package, the RExcel system, the shiny package, and a minimal discussion on writing R packages. There is a new appendix on computational precision illustrating and explaining the FAQ (Frequently Asked Questions) about the differences between the familiar real number system and the less-familiar floating point system used in computers. The probability distributions appendix has been expanded to include more distributions (all the distributions in base R) and to include graphs of each. The editing appendix from the First Edition has been split into four expanded appendices--on working style, writing style, use of a powerful editor, and use of LaTeX for document preparation.
Statistical Analysis in Climate Research
by Hans Von Storch Francis W. ZwiersClimatology is, to a large degree, the study of the statistics of our climate. The powerful tools of mathematical statistics therefore find wide application in climatological research. The purpose of this book is to help the climatologist understand the basic precepts of the statistician's art and to provide some of the background needed to apply statistical methodology correctly and usefully. The book is self contained: introductory material, standard advanced techniques, and the specialised techniques used specifically by climatologists are all contained within this one source. There are a wealth of real-world examples drawn from the climate literature to demonstrate the need, power and pitfalls of statistical analysis in climate research. Suitable for graduate courses on statistics for climatic, atmospheric and oceanic science, this book will also be valuable as a reference source for researchers in climatology, meteorology, atmospheric science, and oceanography.
Statistical Analysis in Forensic Science
by Grzegorz Zadora Daniel Ramos Colin Aitken Agnieszka MartynaA practical guide for determining the evidential value of physicochemical dataMicrotraces of various materials (e.g. glass, paint, fibres, and petroleum products) are routinely subjected to physicochemical examination by forensic experts, whose role is to evaluate such physicochemical data in the context of the prosecution and defence propositions. Such examinations return various kinds of information, including quantitative data. From the forensic point of view, the most suitable way to evaluate evidence is the likelihood ratio. This book provides a collection of recent approaches to the determination of likelihood ratios and describes suitable software, with documentation and examples of their use in practice. The statistical computing and graphics software environment R, pre-computed Bayesian networks using Hugin Researcher and a new package, calcuLatoR, for the computation of likelihood ratios are all explored.Statistical Analysis in Forensic Science will provide an invaluable practical guide for forensic experts and practitioners, forensic statisticians, analytical chemists, and chemometricians.Key features include:Description of the physicochemical analysis of forensic trace evidence.Detailed description of likelihood ratio models for determining the evidential value of multivariate physicochemical data.Detailed description of methods, such as empirical cross-entropy plots, for assessing the performance of likelihood ratio-based methods for evidence evaluation.Routines written using the open-source R software, as well as Hugin Researcher and calcuLatoR.Practical examples and recommendations for the use of all these methods in practice.
Statistical Analysis of Clinical Data on a Pocket Calculator, Part 2
by Aeilko H. Zwinderman Ton J. CleophasThe first part of this title contained all statistical tests relevant to starting clinical investigations, and included tests for continuous and binary data, power, sample size, multiple testing, variability, confounding, interaction, and reliability. The current part 2 of this title reviews methods for handling missing data, manipulated data, multiple confounders, predictions beyond observation, uncertainty of diagnostic tests, and the problems of outliers. Also robust tests, non-linear modeling , goodness of fit testing, Bhatacharya models, item response modeling, superiority testing, variability testing, binary partitioning for CART (classification and regression tree) methods, meta-analysis, and simple tests for incident analysis and unexpected observations at the workplace and reviewed. Each test method is reported together with (1) a data example from practice, (2) all steps to be taken using a scientific pocket calculator, and (3) the main results and their interpretation. Although several of the described methods can also be carried out with the help of statistical software, the latter procedure will be considerably slower. Both part 1 and 2 of this title consist of a minimum of text and this will enhance the process of mastering the methods. Yet the authors recommend that for a better understanding of the test procedures the books be used together with the same authors' textbook "Statistics Applied to Clinical Studies" 5th edition edited 2012, by Springer Dordrecht Netherlands. More complex data files like data files with multiple treatment modalities or multiple predictor variables can not be analyzed with a pocket calculator. We recommend that the small books "SPSS for starters", Part 1 and 2 (Springer, Dordrecht, 2010, and 2012) from the same authors be used as a complementary help for the readers' benefit.
Statistical Analysis of Contingency Tables
by Morten Fagerland Stian Lydersen Petter LaakeStatistical Analysis of Contingency Tables is an invaluable tool for statistical inference in contingency tables. It covers effect size estimation, confidence intervals, and hypothesis tests for the binomial and the multinomial distributions, unpaired and paired 2x2 tables, rxc tables, ordered rx2 and 2xc tables, paired cxc tables, and stratified tables. For each type of table, key concepts are introduced, and a wide range of intervals and tests, including recent and unpublished methods and developments, are presented and evaluated. Topics such as diagnostic accuracy, inter-rater reliability, and missing data are also covered. The presentation is concise and easily accessible for readers with diverse professional backgrounds, with the mathematical details kept to a minimum. For more information, including a sample chapter and software, please visit the authors' website.
The Statistical Analysis of Doubly Truncated Data: With Applications in R (Wiley Series in Probability and Statistics)
by Jacobo de Uña-Álvarez Rosa M. Crujeiras Prof Carla MoreiraA thorough treatment of the statistical methods used to analyze doubly truncated data In The Statistical Analysis of Doubly Truncated Data, an expert team of statisticians delivers an up-to-date review of existing methods used to deal with randomly truncated data, with a focus on the challenging problem of random double truncation. The authors comprehensively introduce doubly truncated data before moving on to discussions of the latest developments in the field. The book offers readers examples with R code along with real data from astronomy, engineering, and the biomedical sciences to illustrate and highlight the methods described within. Linear regression models for doubly truncated responses are provided and the influence of the bandwidth in the performance of kernel-type estimators, as well as guidelines for the selection of the smoothing parameter, are explored. Fully nonparametric and semiparametric estimators are explored and illustrated with real data. R code for reproducing the data examples is also provided. The book also offers: A thorough introduction to the existing methods that deal with randomly truncated data Comprehensive explorations of linear regression models for doubly truncated responses Practical discussions of the influence of bandwidth in the performance of kernel-type estimators and guidelines for the selection of the smoothing parameter In-depth examinations of nonparametric and semiparametric estimators Perfect for statistical professionals with some background in mathematical statistics, biostatisticians, and mathematicians with an interest in survival analysis and epidemiology, The Statistical Analysis of Doubly Truncated Data is also an invaluable addition to the libraries of biomedical scientists and practitioners, as well as postgraduate students studying survival analysis.
Statistical Analysis of Empirical Data: Methods for Applied Sciences
by Scott PardoResearchers and students who use empirical investigation in their work must go through the process of selecting statistical methods for analyses, and they are often challenged to justify these selections. This book is designed for readers with limited background in statistical methodology who seek guidance in defending their statistical decision-making in the worlds of research and practice. It is devoted to helping students and scholars find the information they need to select data analytic methods, and to speak knowledgeably about their statistical research processes. Each chapter opens with a conundrum relating to the selection of an analysis, or to explaining the nature of an analysis. Throughout the chapter, the analysis is described, along with some guidance in justifying the choices of that particular method.Designed to offer statistical knowledge to the non-specialist, this volume can be used in courses on research methods, or for courses on statistical applications to biological, medical, life, social, or physical sciences. It will also be useful to academic and industrial researchers in engineering and in the physical sciences who will benefit from a stronger understanding of how to analyze empirical data. The book is written for those with foundational education in calculus. However, a brief review of fundamental concepts of probability and statistics, together with a primer on some concepts in elementary calculus and matrix algebra, is included. R code and sample datasets are provided.
The Statistical Analysis of Experimental Data
by John MandelThe increasing importance in laboratory situations of minutely precise measurements presents the chemist and physicist with numerous problems in data analysis. National Bureau of Standards statistics consultant John Mandel here draws a clear and fascinating blueprint for a systematic science of statistical analysis -- geared to the particular needs of the physical scientist, with approach and examples aimed specifically at the statistical problems he is likely to confront. The first third of The Statistical Analysis of Experimental Data comprises a thorough grounding in the fundamental mathematical definitions, concepts, and facts underlying modern statistical theory -- math knowledge beyond basic algebra, calculus, and analytic geometry is not required. Remaining chapters deal with statistics as an interpretative tool that can enable the laboratory researcher to determine his most effective methodology. You'll find lucid, concise coverage of over 130 topics, including elements of measurement; nature of statistical analysis; design/analysis of experiments; statistics as diagnostic tool; precision and accuracy; testing statistical models; between-within classifications; two-way classifications; sampling (principles, objectives, methods); fitting of non-linear models; measurement of processes; components of variance; nested designs; the sensitivity ratio, and much more.Also included are many examples, each worked in step-by-step fashion; nearly 200 helpful figures and tables; and concluding chapter summaries followed by references for further study.Mandel argues that, when backed by an understanding of its theoretic framework, statistics offers researchers "not only a powerful tool for the interpretation of experiments but also a task of real intellectual gratification." The Statistical Analysis of Experimental Data provides the physical scientist with the explanations and models he requires to impress this invaluable tool into service.
Statistical Analysis of Financial Data: With Examples In R (Chapman & Hall/CRC Texts in Statistical Science)
by James GentleStatistical Analysis of Financial Data covers the use of statistical analysis and the methods of data science to model and analyze financial data. The first chapter is an overview of financial markets, describing the market operations and using exploratory data analysis to illustrate the nature of financial data. The software used to obtain the data for the examples in the first chapter and for all computations and to produce the graphs is R. However discussion of R is deferred to an appendix to the first chapter, where the basics of R, especially those most relevant in financial applications, are presented and illustrated. The appendix also describes how to use R to obtain current financial data from the internet. Chapter 2 describes the methods of exploratory data analysis, especially graphical methods, and illustrates them on real financial data. Chapter 3 covers probability distributions useful in financial analysis, especially heavy-tailed distributions, and describes methods of computer simulation of financial data. Chapter 4 covers basic methods of statistical inference, especially the use of linear models in analysis, and Chapter 5 describes methods of time series with special emphasis on models and methods applicable to analysis of financial data. Features * Covers statistical methods for analyzing models appropriate for financial data, especially models with outliers or heavy-tailed distributions. * Describes both the basics of R and advanced techniques useful in financial data analysis. * Driven by real, current financial data, not just stale data deposited on some static website. * Includes a large number of exercises, many requiring the use of open-source software to acquire real financial data from the internet and to analyze it.
Statistical Analysis of Gene Expression Microarray Data (Chapman And Hall/crc Interdisciplinary Statistics Ser.)
by Terry SpeedAlthough less than a decade old, the field of microarray data analysis is now thriving and growing at a remarkable pace. Biologists, geneticists, and computer scientists as well as statisticians all need an accessible, systematic treatment of the techniques used for analyzing the vast amounts of data generated by large-scale gene expression studies
Statistical Analysis of Graph Structures in Random Variable Networks (SpringerBriefs in Optimization)
by V. A. Kalyagin A. P. Koldanov P. A. Koldanov P. M. PardalosThis book studies complex systems with elements represented by random variables. Its main goal is to study and compare uncertainty of algorithms of network structure identification with applications to market network analysis. For this, a mathematical model of random variable network is introduced, uncertainty of identification procedure is defined through a risk function, random variables networks with different measures of similarity (dependence) are discussed, and general statistical properties of identification algorithms are studied. The volume also introduces a new class of identification algorithms based on a new measure of similarity and prove its robustness in a large class of distributions, and presents applications to social networks, power transmission grids, telecommunication networks, stock market networks, and brain networks through a theoretical analysis that identifies network structures. Both researchers and graduate students in computer science, mathematics, and optimization will find the applications and techniques presented useful.
Statistical Analysis of Human Growth and Development (Chapman & Hall/CRC Biostatistics Series)
by Yin Bun CheungStatistical Analysis of Human Growth and Development is an accessible and practical guide to a wide range of basic and advanced statistical methods that are useful for studying human growth and development. Designed for nonstatisticians and statisticians new to the analysis of growth and development data, the book collects methods scattered through
Statistical Analysis of Management Data
by Hubert GatignonStatistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures. Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.
STATISTICAL ANALYSIS OF MASSIVE DATA STREAMS: Proceedings of a Workshop
by Committee on Applied Theoretical StatisticsMassive data streams—large quantities of data that arrive continuously—are becoming increasingly commonplace in many areas of science and technology. Consequently development of analytical methods for such streams is of growing importance. To address this issue, the National Security Agency asked the NRC to hold a workshop to explore methods for analysis of streams of data so as to stimulate progress in the field. This report presents the results of that workshop. It provides presentations that focused on five different research areas where massive data streams are present: atmospheric and meteorological data; high-energy physics; integrated data systems; network traffic; and mining commercial data streams. The goals of the report are to improve communication among researchers in the field and to increase relevant statistical science activity.
Statistical Analysis of Microbiome Data (Frontiers in Probability and the Statistical Sciences)
by Somnath Datta Subharup GuhaMicrobiome research has focused on microorganisms that live within the human body and their effects on health. During the last few years, the quantification of microbiome composition in different environments has been facilitated by the advent of high throughput sequencing technologies. The statistical challenges include computational difficulties due to the high volume of data; normalization and quantification of metabolic abundances, relative taxa and bacterial genes; high-dimensionality; multivariate analysis; the inherently compositional nature of the data; and the proper utilization of complementary phylogenetic information. This has resulted in an explosion of statistical approaches aimed at tackling the unique opportunities and challenges presented by microbiome data. This book provides a comprehensive overview of the state of the art in statistical and informatics technologies for microbiome research. In addition to reviewing demonstrably successful cutting-edge methods, particular emphasis is placed on examples in R that rely on available statistical packages for microbiome data. With its wide-ranging approach, the book benefits not only trained statisticians in academia and industry involved in microbiome research, but also other scientists working in microbiomics and in related fields.
The Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach (Chapman & Hall/CRC Monographs on Statistics and Applied Probability #1)
by Shanshan Zhao Ross PrenticeThe Statistical Analysis of Multivariate Failure Time Data: A Marginal Modeling Approach provides an innovative look at methods for the analysis of correlated failure times. The focus is on the use of marginal single and marginal double failure hazard rate estimators for the extraction of regression information. For example, in a context of randomized trial or cohort studies, the results go beyond that obtained by analyzing each failure time outcome in a univariate fashion. The book is addressed to researchers, practitioners, and graduate students, and can be used as a reference or as a graduate course text. Much of the literature on the analysis of censored correlated failure time data uses frailty or copula models to allow for residual dependencies among failure times, given covariates. In contrast, this book provides a detailed account of recently developed methods for the simultaneous estimation of marginal single and dual outcome hazard rate regression parameters, with emphasis on multiplicative (Cox) models. Illustrations are provided of the utility of these methods using Women’s Health Initiative randomized controlled trial data of menopausal hormones and of a low-fat dietary pattern intervention. As byproducts, these methods provide flexible semiparametric estimators of pairwise bivariate survivor functions at specified covariate histories, as well as semiparametric estimators of cross ratio and concordance functions given covariates. The presentation also describes how these innovative methods may extend to handle issues of dependent censorship, missing and mismeasured covariates, and joint modeling of failure times and covariates, setting the stage for additional theoretical and applied developments. This book extends and continues the style of the classic Statistical Analysis of Failure Time Data by Kalbfleisch and Prentice. Ross L. Prentice is Professor of Biostatistics at the Fred Hutchinson Cancer Research Center and University of Washington in Seattle, Washington. He is the recipient of COPSS Presidents and Fisher awards, the AACR Epidemiology/Prevention and Team Science awards, and is a member of the National Academy of Medicine. Shanshan Zhao is a Principal Investigator at the National Institute of Environmental Health Sciences in Research Triangle Park, North Carolina.
Statistical Analysis of Network Data with R (Use R! #65)
by Eric D. Kolaczyk Gábor CsárdiThe new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.
Statistical Analysis of Operational Risk Data (SpringerBriefs in Statistics)
by Danilo Carità Francesco Martinelli Giovanni De LucaThis concise book for practitioners presents the statistical analysis of operational risk, which is considered the most relevant source of bank risk, after market and credit risk. The book shows that a careful statistical analysis can improve the results of the popular loss distribution approach. The authors identify the risk classes by applying a pooling rule based on statistical tests of goodness-of-fit, use the theory of the mixture of distributions to analyze the loss severities, and apply copula functions for risk class aggregation. Lastly, they assess operational risk data in order to estimate the so-called capital-at-risk that represents the minimum capital requirement that a bank has to hold. The book is primarily intended for quantitative analysts and risk managers, but also appeals to graduate students and researchers interested in bank risks.
Statistical Analysis of Panel Count Data
by Jianguo Sun Xingqiu ZhaoPanel count data occur in studies that concern recurrent events, or event history studies, when study subjects are observed only at discrete time points. By recurrent events, we mean the event that can occur or happen multiple times or repeatedly. Examples of recurrent events include disease infections, hospitalizations in medical studies, warranty claims of automobiles or system break-downs in reliability studies. In fact, many other fields yield event history data too such as demographic studies, economic studies and social sciences. For the cases where the study subjects are observed continuously, the resulting data are usually referred to as recurrent event data. This book collects and unifies statistical models and methods that have been developed for analyzing panel count data. It provides the first comprehensive coverage of the topic. The main focus is on methodology, but for the benefit of the reader, the applications of the methods to real data are also discussed along with numerical calculations. There exists a great deal of literature on the analysis of recurrent event data. This book fills the void in the literature on the analysis of panel count data. This book provides an up-to-date reference for scientists who are conducting research on the analysis of panel count data. It will also be instructional for those who need to analyze panel count data to answer substantive research questions. In addition, it can be used as a text for a graduate course in statistics or biostatistics that assumes a basic knowledge of probability and statistics.
Statistical Analysis of Reliability and Life-Testing Models: Theory and Methods (Second Edition) (Statistics: Textbooks and Monographs #115)
by Lee BainTextbook for a methods course or reference for an experimenter who is mainly interested in data analyses rather than in the mathematical development of the procedures.
Statistical Analysis of Reliability Data (Chapman And Hall/crc Texts In Statistical Science Ser. #27)
by Martin J. CrowderWritten for those who have taken a first course in statistical methods, this book takes a modern, computer-oriented approach to describe the statistical techniques used for the assessment of reliability.