Industrial Statistics. Practical Methods and Guidance for Improved Performance

Now in its second edition,  Practical Statistics for Nursing and Health Care  provides a sound foundation for nursing, midwifery and other health care students and early career professionals, guiding readers through the often daunting subject of statistics ‘from scratch’. Making no assumptions about one’s existing knowledge, the text develops in complexity as the material and concepts become more familiar, allowing readers to build the confidence and skills to apply various formula and techniques to their own data.  The authors explain common methods of interpreting data sets and explore basic statistical principles that enable nurses and health care professionals to decide on suitable treatment, as well as equipping readers with the tools to critically appraise clinical trials and epidemiology journals.  Offers information on statistics presented in a clear, straightforward manner Covers all basic statistical concepts and tests, and includes worked examples, case studies, and data sets Provides an understanding of how data collected can be processed for the patients’ benefit Contains a new section on how to calculate and use percentiles Written for students, qualified nurses and other healthcare professionals,  Practical Statistics for Nursing and Health Care  is a hands-on guide to gaining rapid proficiency in statistics.

Nursing is a growing area of higher education, in which an introduction to statistics is an essential component. There is currently a gap in the market for a 'user-friendly' book which is contextulised and targeted for nursing. Practical Statistics for Nursing and Health Care introduces statistical techniques in such a way that readers will easily grasp the fundamentals to enable them to gain the confidence and understanding to perform their own analysis. It also provides sufficient advice in areas such as clinical trials and epidemiology to enable the reader to critically appraise work published in journals such as the Lancet and British Medical Journal. * Covers all basic statistical concepts and tests * Is user-friendly – avoids excessive jargon * Includes relevant examples for nurses, including case studies and data sets * Provides information on further reading * Starts from first principles and progresses step by step * Includes 'advice on' sections for all of the tests described

Biocatalysts are increasingly used by chemists engaged in fine chemical synthesis within both industry and academia. Today, there exists a huge choice of high-tech enzymes and whole cell biocatalysts, which add enormously to the repertoire of synthetic possibilities. Practical Methods for Biocatalysis and Biotransformations 3 will be a companion book to Practical Methods for Biocatalysis and Biotransformations (2009) and Practical Methods for Biocatalysis and Biotransformations 2 (2012). Following the successful format of the two volumes, it will be a “how-to” guide focusing on commercially available enzymes and strains of microorganisms that are readily obtained from culture collections. The source of starting materials and reagents, hints, tips and safety advice (where appropriate) will be given to ensure, as far as possible, that the procedures are reproducible. Comparisons to alternative methodology will be given and relevant references to the primary literature will be cited. Contents include: Biotransformation Process Technology Industrial Biooxidation Hydrolase catalysed hydrolysis/synthesis Reduction Oxidation Halogenation Transferase catalysed glycosylation, methylation, etc C-C bond formation Tandem Biocatalytic Reactions Practical Methods for Biocatalysis and Biotransformations, Volume 3 is an essential collection of validated biocatalytic methods which will find a place on the bookshelves of synthetic organic chemists, pharmaceutical chemists, and process R&D chemists in industry and academia.

A new edition of this popular text on robust statistics, thoroughly updated to include new and improved methods and focus on implementation of methodology using the increasingly popular open-source software R. Classical statistics fail to cope well with outliers associated with deviations from standard distributions. Robust statistical methods take into account these deviations when estimating the parameters of parametric models, thus increasing the reliability of fitted models and associated inference. This new, second edition of Robust Statistics: Theory and Methods (with R) presents a broad coverage of the theory of robust statistics that is integrated with computing methods and applications. Updated to include important new research results of the last decade and focus on the use of the popular software package R, it features in-depth coverage of the key methodology, including regression, multivariate analysis, and time series modeling. The book is illustrated throughout by a range of examples and applications that are supported by a companion website featuring data sets and R code that allow the reader to reproduce the examples given in the book. Unlike other books on the market, Robust Statistics: Theory and Methods (with R) offers the most comprehensive, definitive, and up-to-date treatment of the subject. It features chapters on estimating location and scale; measuring robustness; linear regression with fixed and with random predictors; multivariate analysis; generalized linear models; time series; numerical algorithms; and asymptotic theory of M-estimates. Explains both the use and theoretical justification of robust methods Guides readers in selecting and using the most appropriate robust methods for their problems Features computational algorithms for the core methods Robust statistics research results of the last decade included in this 2nd edition include: fast deterministic robust regression, finite-sample robustness, robust regularized regression, robust location and scatter estimation with missing data, robust estimation with independent outliers in variables, and robust mixed linear models. Robust Statistics aims to stimulate the use of robust methods as a powerful tool to increase the reliability and accuracy of statistical modelling and data analysis. It is an ideal resource for researchers, practitioners, and graduate students in statistics, engineering, computer science, and physical and social sciences.

FAIR VALUE MEASUREMENT Practical Guidance and Implementation «Writing Fair Value Measurement was a monumental task fraught with controversy not only in the U.S., but also abroad. Having coauthored a book with Mark as well as written one myself, I was even more impressed that Mark was able to navigate these fair value minefields and produce a work that will be a tremendous help to accountants and non-accountants alike. If you are looking for a 'one-stop' book on fair value measurement, this is it!» —Neil J. Beaton, CPA/ABV, CFA, ASA, Managing Director, Valuation Services, Alvarez & Marsal «In recent years, the FASB and the IASB have added many requirements for various assets and liabilities to be measured at fair value. In this book, Zyla clearly describes how to implement fair value measurement and how investors and creditors should interpret it. The crisp writing and illuminating analysis will help readers to grasp the essence of fair value accounting and to apply it wisely.» —J. Edward Ketz, Associate Professor of Accounting, Pennsylvania State University «The focus of the book is on fair value measurement based on level 3 inputs. This is a critical area of valuations for financial reporting due to a false comfort given by routine application of evaluating models and assumptions without real possibilities of calibration. Mark Zyla offers an extraordinary help in understanding the reliability of the inputs and the outputs of the different methodologies and the inherent biases in each of them. The book is a precious support for better valuation both in U.S. GAAP and in IFRS fair value reporting.» —Mauro Bini, Full Professor of Corporate Finance, Bocconi University; Chairman, Management Board OIV (Organismo Italiano di Valutazione) Essential guidance on the fair value measurement process Now in a second edition, Fair Value Measurement: Practical Guidance and Implementation helps you succeed in understanding the fair value accounting rules that entities must follow. The result is a resource that you can rely on to understand the importance of valuation and the concepts that define it. In basic, nontechnical language, author Mark Zyla presents practical direction for best practices of financial valuation as well as for understanding the many FASB pronouncements involving fair value. An essential guide for auditors and valuation specialists, Fair Value Measurement: Practical Guidance and Implementation, Second Edition brings you up to speed on what fair value really means.

Mathematical finance has grown into a huge area of research which requires a lot of care and a large number of sophisticated mathematical tools. Mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, it considers various aspects of the application of statistical methods in finance and illustrates some of the many ways that statistical tools are used in financial applications. Financial Statistics and Mathematical Finance: Provides an introduction to the basics of financial statistics and mathematical finance. Explains the use and importance of statistical methods in econometrics and financial engineering. Illustrates the importance of derivatives and calculus to aid understanding in methods and results. Looks at advanced topics such as martingale theory, stochastic processes and stochastic integration. Features examples throughout to illustrate applications in mathematical and statistical finance. Is supported by an accompanying website featuring R code and data sets. Financial Statistics and Mathematical Finance introduces the financial methodology and the relevant mathematical tools in a style that is both mathematically rigorous and yet accessible to advanced level practitioners and mathematicians alike, both graduate students and researchers in statistics, finance, econometrics and business administration will benefit from this book.

A highly accessible alternative approach to basic statistics Praise for the First Edition: «Certainly one of the most impressive little paperback 200-page introductory statistics books that I will ever see . . . it would make a good nightstand book for every statistician.»—Technometrics Written in a highly accessible style, Introduction to Statistics through Resampling Methods and R, Second Edition guides students in the understanding of descriptive statistics, estimation, hypothesis testing, and model building. The book emphasizes the discovery method, enabling readers to ascertain solutions on their own rather than simply copy answers or apply a formula by rote. The Second Edition utilizes the R programming language to simplify tedious computations, illustrate new concepts, and assist readers in completing exercises. The text facilitates quick learning through the use of: More than 250 exercises—with selected «hints»—scattered throughout to stimulate readers' thinking and to actively engage them in applying their newfound skills An increased focus on why a method is introduced Multiple explanations of basic concepts Real-life applications in a variety of disciplines Dozens of thought-provoking, problem-solving questions in the final chapter to assist readers in applying statistics to real-life applications Introduction to Statistics through Resampling Methods and R, Second Edition is an excellent resource for students and practitioners in the fields of agriculture, astrophysics, bacteriology, biology, botany, business, climatology, clinical trials, economics, education, epidemiology, genetics, geology, growth processes, hospital administration, law, manufacturing, marketing, medicine, mycology, physics, political science, psychology, social welfare, sports, and toxicology who want to master and learn to apply statistical methods.

This publication includes invaluable guidance for anyone responsible for or advising on an enterprise risk management process (ERM), whether the process is in its early stages or is already well established. This resource will help ensure the ERM process is well designed, well executed, and ultimately successful. Global, economic, and regulatory conditions as well as everyday internal risks can affect business operations, so it is important to have a process in place that identifies these events and manages risks. This guide leverages the concepts of existing frameworks as a foundation for providing illustrative examples, best practices, and guidance for implementing or assessing an enterprise risk management process.

A comprehensive look at how probability and statistics is applied to the investment process Finance has become increasingly more quantitative, drawing on techniques in probability and statistics that many finance practitioners have not had exposure to before. In order to keep up, you need a firm understanding of this discipline. Probability and Statistics for Finance addresses this issue by showing you how to apply quantitative methods to portfolios, and in all matter of your practices, in a clear, concise manner. Informative and accessible, this guide starts off with the basics and builds to an intermediate level of mastery. • Outlines an array of topics in probability and statistics and how to apply them in the world of finance • Includes detailed discussions of descriptive statistics, basic probability theory, inductive statistics, and multivariate analysis • Offers real-world illustrations of the issues addressed throughout the text The authors cover a wide range of topics in this book, which can be used by all finance professionals as well as students aspiring to enter the field of finance.