Books in the Statistics in Practice series

There are many factors that environmental scientists should consider in their research. Weather and climate vary widely between locations, soil varies at every spatial scale at which it is examined, and even man-made attributes, such as the distribution of pollution, fluctuate significantly.

Modern Industrial StatisticsThe new edition of the prime reference on the tools of statistics used in industry and services, integrating theoretical, practical, and computer-based approachesModern Industrial Statistics is a leading reference and guide to the statistics tools widely used in industry and services. Designed to help professionals and students easily access relevant theoretical and practical information in a single volume, this standard resource employs a computer-intensive approach to industrial statistics and provides numerous examples and procedures in the popular R language and for MINITAB and JMP statistical analysis software. Divided into two parts, the text covers the principles of statistical thinking and analysis, bootstrapping, predictive analytics, Bayesian inference, time series analysis, acceptance sampling, statistical process control, design and analysis of experiments, simulation and computer experiments, and reliability and survival analysis. Part A, on computer age statistical analysis, can be used in general courses on analytics and statistics. Part B is focused on industrial statistics applications.The fully revised third edition covers the latest techniques in R, MINITAB and JMP, and features brand-new coverage of time series analysis, predictive analytics and Bayesian inference. New and expanded simulation activities, examples, and case studies--drawn from the electronics, metal work, pharmaceutical, and financial industries--are complemented by additional computer and modeling methods. Helping readers develop skills for modeling data and designing experiments, this comprehensive volume:* Explains the use of computer-based methods such as bootstrapping and data visualization* Covers nonstandard techniques and applications of industrial statistical process control (SPC) charts* Contains numerous problems, exercises, and data sets representing real-life case studies of statistical work in various business and industry settings* Includes access to a companion website that contains an introduction to R, sample R code, csv files of all data sets, JMP add-ins, and downloadable appendices* Provides an author-created R package, mistat, that includes all data sets and statistical analysis applications used in the bookPart of the acclaimed Statistics in Practice series, Modern Industrial Statistics with Applications in R, MINITAB, and JMP, Third Edition, is the perfect textbook for advanced undergraduate and postgraduate courses in the areas of industrial statistics, quality and reliability engineering, and an important reference for industrial statisticians, researchers, and practitioners in related fields. The mistat R-package is available from the R CRAN repository.

Statistical Issues in Drug DevelopmentThe revised third edition of Statistical Issues in Drug Development delivers an insightful treatment of the intersection between statistics and the life sciences. The book offers readers new discussions of crucial topics, including cluster randomization, historical controls, responder analysis, studies in children, post-hoc tests, estimands, publication bias, the replication crisis, and many more.This work presents the major statistical issues in drug development in a way that is accessible and comprehensible to life scientists working in the field, and takes pains not to gloss over significant disagreements in the field of statistics, while encouraging communication between the statistical and life sciences disciplines. In addition to new material on topics like invalid inversion, severity, random effects in network meta-analysis, and explained variation, readers will benefit from the inclusion of:* A thorough introduction to basic topics in drug development and statistics, including the role played by statistics in drug development* An exploration of the four views of statistics in drug development, including the historical, methodological, technical, and professional* An examination of debatable and controversial topics in drug development, including the allocation of treatments to patients in clinical trials, baselines and covariate information, and the measurement of treatment effectsPerfect for life scientists and other professionals working in the field of drug development, Statistical Issues in Drug Development is the ideal resource for anyone seeking a one-stop reference to enhance their understanding of the use of statistics during drug development.

Individual Participant Data Meta-Analysis: A Handbook for Healthcare Research provides a comprehensive introduction to the fundamental principles and methods that healthcare researchers need when considering, conducting or using individual participant data (IPD) meta-analysis projects. Written and edited by researchers with substantial experience in the field, the book details key concepts and practical guidance for each stage of an IPD meta-analysis project, alongside illustrated examples and summary learning points.Split into five parts, the book chapters take the reader through the journey from initiating and planning IPD projects to obtaining, checking, and meta-analysing IPD, and appraising and reporting findings. The book initially focuses on the synthesis of IPD from randomised trials to evaluate treatment effects, including the evaluation of participant-level effect modifiers (treatment-covariate interactions). Detailed extension is then made to specialist topics such as diagnostic test accuracy, prognostic factors, risk prediction models, and advanced statistical topics such as multivariate and network meta-analysis, power calculations, and missing data.Intended for a broad audience, the book will enable the reader to:* Understand the advantages of the IPD approach and decide when it is needed over a conventional systematic review* Recognise the scope, resources and challenges of IPD meta-analysis projects* Appreciate the importance of a multi-disciplinary project team and close collaboration with the original study investigators* Understand how to obtain, check, manage and harmonise IPD from multiple studies* Examine risk of bias (quality) of IPD and minimise potential biases throughout the project* Understand fundamental statistical methods for IPD meta-analysis, including two-stage and one-stage approaches (and their differences), and statistical software to implement them* Clearly report and disseminate IPD meta-analyses to inform policy, practice and future research* Critically appraise existing IPD meta-analysis projects* Address specialist topics such as effect modification, multiple correlated outcomes, multiple treatment comparisons, non-linear relationships, test accuracy at multiple thresholds, multiple imputation, and developing and validating clinical prediction modelsDetailed examples and case studies are provided throughout.

Statistics and the Evaluation of Evidence for Forensic ScientistsThe leading resource in the statistical evaluation and interpretation of forensic evidenceThe third edition of Statistics and the Evaluation of Evidence for Forensic Scientists is fully updated to provide the latest research and developments in the use of statistical techniques to evaluate and interpret evidence. Courts are increasingly aware of the importance of proper evidence assessment when there is an element of uncertainty. Because of the increasing availability of data, the role of statistical and probabilistic reasoning is gaining a higher profile in criminal cases. That's why lawyers, forensic scientists, graduate students, and researchers will find this book an essential resource, one which explores how forensic evidence can be evaluated and interpreted statistically. It's written as an accessible source of information for all those with an interest in the evaluation and interpretation of forensic scientific evidence.* Discusses the entire chain of reasoning-from evidence pre-assessment to court presentation;* Includes material for the understanding of evidence interpretation for single and multiple trace evidence;* Provides real examples and data for improved understanding.Since the first edition of this book was published in 1995, this respected series has remained a leading resource in the statistical evaluation of forensic evidence. It shares knowledge from authors in the fields of statistics and forensic science who are international experts in the area of evidence evaluation and interpretation. This book helps people to deal with uncertainty related to scientific evidence and propositions. It introduces a method of reasoning that shows how to update beliefs coherently and to act rationally. In this edition, readers can find new information on the topics of elicitation, subjective probabilities, decision analysis, and cognitive bias, all discussed in a Bayesian framework.

This book provides an up-to-date, comprehensive, clinically oriented account of the molecular biology, pathogenesis, clinical features, diagnosis and management of human bacterial diseases, as well as their control and prevention.

A mixed model allows the incorporation of both fixed and random variables within a statistical analysis. This enables efficient inferences and more information to be gained from the data. The application of mixed models is an increasingly popular way of analysing medical data, particularly in the pharmaceutical industry.

Offers guidance to forensic scientists with little dependence on mathematical ability. This book helps to assess DNA evidence and present that evidence in a courtroom setting.

There has been substantial growth in the use of data monitoring committees in recent years, particularly within the pharmaceutical industry. This growth has brought with it increasing uncertainty regarding regulatory compliance, and the statistician or clinical trials co-ordinator often has to struggle with conflicting information.

Presents a multitude of topics relevant to the quantitative finance community by combining the best of the theory with the usefulness of applicationsWritten by accomplished teachers and researchers in the field, this book presents quantitative finance theory through applications to specific practical problems and comes with accompanying coding techniques in R and MATLAB, and some generic pseudo-algorithms to modern finance. It also offers over 300 examples and exercises that are appropriate for the beginning student as well as the practitioner in the field.The Quantitative Finance book is divided into four parts. Part One begins by providing readers with the theoretical backdrop needed from probability and stochastic processes. We also present some useful finance concepts used throughout the book. In part two of the book we present the classical Black-Scholes-Merton model in a uniquely accessible and understandable way. Implied volatility as well as local volatility surfaces are also discussed. Next, solutions to Partial Differential Equations (PDE), wavelets and Fourier transforms are presented. Several methodologies for pricing options namely, tree methods, finite difference method and Monte Carlo simulation methods are also discussed. We conclude this part with a discussion on stochastic differential equations (SDE's). In the third part of this book, several new and advanced models from current literature such as general Lvy processes, nonlinear PDE's for stochastic volatility models in a transaction fee market, PDE's in a jump-diffusion with stochastic volatility models and factor and copulas models are discussed. In part four of the book, we conclude with a solid presentation of the typical topics in fixed income securities and derivatives. We discuss models for pricing bonds market, marketable securities, credit default swaps (CDS) and securitizations.* Classroom-tested over a three-year period with the input of students and experienced practitioners* Emphasizes the volatility of financial analyses and interpretations* Weaves theory with application throughout the book* Utilizes R and MATLAB software programs* Presents pseudo-algorithms for readers who do not have access to any particular programming system* Supplemented with extensive author-maintained web site that includes helpful teaching hints, data sets, software programs, and additional contentQuantitative Finance is an ideal textbook for upper-undergraduate and beginning graduate students in statistics, financial engineering, quantitative finance, and mathematical finance programs. It will also appeal to practitioners in the same fields.

A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective.

A comprehensive and practical resource for analyses of crossover designs For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible.

Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method.

Statistical Applications for Environmental Analysis and Risk Assessment guides readers through real-world situations and the best statistical methods used to determine the nature and extent of the problem, evaluate the potential human health and ecological risks, and design and implement remedial systems as necessary.

"This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation" Dr.

A much-needed guide to the design and analysis of cluster randomized trials, How to Design, Analyse and Report Cluster Randomised Trials in Medicine and Health Related Research delivers practical guidance on the design and analysis of cluster randomised trials (cRCTs) in healthcare research.

This book provides a modern, hands-on guide to the essential concepts and ideas for analyzing data with missing observations in the field of biostatistics. It acknowledges the limitations of established techniques and provides concrete applications of newly developed methods.

An authoritative introduction to efficiency and productivity analysis with applications in both the banking and finance industry In light of the recent global financial crisis, several studies have examined the efficiency of financial institutions.

A practical guide for handling and planning for missing data in clinical trials, Clinical Trials with Missing Data provides a comprehensive account of the problems arising when data from clinical and related studies are incomplete, and presents statisticians, biostatisticians, and researchers with approaches to effectively address them.

Hospitals monitoring is becoming more complex and is increasing both because staff want their data analysed and because of increasing mandated surveillance.

A comprehensive and practical overview of the identification, conduct and analysis of optimal Phase II trial design.

Explores computer-intensive probability and statistics for ecosystem management decision making Simulation is an accessible way to explain probability and stochastic model behavior to beginners.

Bridging classic ideas with modern statistical modeling concepts and the latest computational methods, Spatio-temporal Design offers a state-of-the-art account of how to collect space-time data for monitoring.

An essential, up-to-date guide to the design of studies and selection of the correct QoL instruments for observational studies and clinical trials. Quality of Life (QoL) outcomes or Person/Patient Reported Outcome Measures (PROMs) are now frequently being used in randomised controlled trials (RCTs) and observational studies.

Supported by a website featuring computer code, data used in the book, and tutorials and solutions, Improving Natural Resource Management demonstrates how to find the most politically acceptable but effective plan for managing an at-risk ecosystem.

* Presents a much-needed practical guide to statistical spatial analysis on a network, in a logical, user-friendly order. * Introduces the preliminary methods involved, before detailing the advanced, computational methods, enabling the readers a complete understanding of the advanced topics.

Customer survey studies deals with customers, consumers and user satisfaction from a product or service. In practice, many of the customer surveys conducted by business and industry are analyzed in a very simple way, without using models or statistical methods. Typical reports include descriptive statistics and basic graphical displays.

This book addresses the statistical, practical, and ethical issues arising from allocating groups of individuals, or clusters, to different interventions.