The book provides a comprehensive treatment of statistical inference using permutation techniques. It features a variety of useful and powerful data analytic tools that rely on very few distributional assumptions. Although many of these procedures have appeared in journal articles, they are not readily available to practitioners.
NONPARAMETRIC STATISTICS WITH APPLICATIONS TO SCIENCE AND ENGINEERING WITH R Introduction to the methods and techniques of traditional and modern nonparametric statistics, incorporating R code Nonparametric Statistics with Applications to Science and Engineering with R presents modern nonparametric statistics from a practical point of view, with the newly revised edition including custom R functions implementing nonparametric methods to explain how to compute them and make them more comprehensible. Relevant built-in functions and packages on CRAN are also provided with a sample code. R codes in the new edition not only enable readers to perform nonparametric analysis easily, but also to visualize and explore data using R’s powerful graphic systems, such as ggplot2 package and R base graphic system. The new edition includes useful tables at the end of each chapter that help the reader find data sets, files, functions, and packages that are used and relevant to the respective chapter. New examples and exercises that enable readers to gain a deeper insight into nonparametric statistics and increase their comprehension are also included. Some of the sample topics discussed in Nonparametric Statistics with Applications to Science and Engineering with R include: Basics of probability, statistics, Bayesian statistics, order statistics, Kolmogorov–Smirnov test statistics, rank tests, and designed experiments Categorical data, estimating distribution functions, density estimation, least squares regression, curve fitting techniques, wavelets, and bootstrap sampling EM algorithms, statistical learning, nonparametric Bayes, WinBUGS, properties of ranks, and Spearman coefficient of rank correlation Chi-square and goodness-of-fit, contingency tables, Fisher exact test, MC Nemar test, Cochran’s test, Mantel–Haenszel test, and Empirical Likelihood Nonparametric Statistics with Applications to Science and Engineering with R is a highly valuable resource for graduate students in engineering and the physical and mathematical sciences, as well as researchers who need a more comprehensive, but succinct understanding of modern nonparametric statistical methods.
This book takes a unique approach to explaining permutation statistics by integrating permutation statistical methods with a wide range of classical statistical methods and associated R programs. It opens by comparing and contrasting two models of statistical inference: the classical population model espoused by J. Neyman and E.S. Pearson and the permutation model first introduced by R.A. Fisher and E.J.G. Pitman. Numerous comparisons of permutation and classical statistical methods are presented, supplemented with a variety of R scripts for ease of computation. The text follows the general outline of an introductory textbook in statistics with chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, completely-randomized analysis of variance, randomized-blocks analysis of variance, simple linear regression and correlation, and the analysis of goodness of fit and contingency. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity, depend only on the observed data, and do not require random sampling. The methods are relatively new in that it took modern computing power to make them available to those working in mainstream research. Designed for an audience with a limited statistical background, the book can easily serve as a textbook for undergraduate or graduate courses in statistics, psychology, economics, political science or biology. No statistical training beyond a first course in statistics is required, but some knowledge of, or some interest in, the R programming language is assumed.
Make data-driven, informed decisions and enhance your statistical expertise in Python by turning raw data into meaningful insights Purchase of the print or Kindle book includes a free PDF eBook Key Features Gain expertise in identifying and modeling patterns that generate success Explore the concepts with Python using important libraries such as stats models Learn how to build models on real-world data sets and find solutions to practical challenges Book DescriptionThe ability to proficiently perform statistical modeling is a fundamental skill for data scientists and essential for businesses reliant on data insights. Building Statistical Models with Python is a comprehensive guide that will empower you to leverage mathematical and statistical principles in data assessment, understanding, and inference generation. This book not only equips you with skills to navigate the complexities of statistical modeling, but also provides practical guidance for immediate implementation through illustrative examples. Through emphasis on application and code examples, you’ll understand the concepts while gaining hands-on experience. With the help of Python and its essential libraries, you’ll explore key statistical models, including hypothesis testing, regression, time series analysis, classification, and more. By the end of this book, you’ll gain fluency in statistical modeling while harnessing the full potential of Python's rich ecosystem for data analysis.What you will learn Explore the use of statistics to make decisions under uncertainty Answer questions about data using hypothesis tests Understand the difference between regression and classification models Build models with stats models in Python Analyze time series data and provide forecasts Discover Survival Analysis and the problems it can solve Who this book is forIf you are looking to get started with building statistical models for your data sets, this book is for you! Building Statistical Models in Python bridges the gap between statistical theory and practical application of Python. Since you’ll take a comprehensive journey through theory and application, no previous knowledge of statistics is required, but some experience with Python will be useful.
Are you about to embark on a research project for the first time? Unsure which data collection methods are right for your study? Don't know where to start? By presenting the reader with a series of key research management questions, this book introduces the novice researcher to a range of research designs and data collection methods. Building an understanding of these choices and how they can impact on the dissertation itself will lead to a more robust and rigorous dissertation study. This book is designed to direct your research choices with informative text and key questions, advice from "virtual supervisors" and reflections from students. Lists of suggested further reading also help to support you on your journey to developing an organised and successful dissertation project. Researchers seeking support on their journey to a successful dissertation will find this book a valuable resource.
This research monograph utilizes exact and Monte Carlo permutation statistical methods to generate probability values and measures of effect size for a variety of measures of association. Association is broadly defined to include measures of correlation for two interval-level variables, measures of association for two nominal-level variables or two ordinal-level variables, and measures of agreement for two nominal-level or two ordinal-level variables. Additionally, measures of association for mixtures of the three levels of measurement are considered: nominal-ordinal, nominal-interval, and ordinal-interval measures. Numerous comparisons of permutation and classical statistical methods are presented. Unlike classical statistical methods, permutation statistical methods do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This book takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field. This topic is relatively new in that it took modern computing power to make permutation methods available to those working in mainstream research. Written for a statistically informed audience, it is particularly useful for teachers of statistics, practicing statisticians, applied statisticians, and quantitative graduate students in fields such as psychology, medical research, epidemiology, public health, and biology. It can also serve as a textbook in graduate courses in subjects like statistics, psychology, and biology.
Written to match to the OCR(A) A Level specification, this text provides individual, board-specific textbooks for each module. Accessible for all levels of student, the series provides pre-AS material in module books to support weaker candidates.
Need to learn statistics as part of your job, or want some help passing a statistics course? Statistics in a Nutshell is a clear and concise introduction and reference that's perfect for anyone with no previous background in the subject. This book gives you a solid understanding of statistics without being too simple, yet without the numbing complexity of most college texts. You get a firm grasp of the fundamentals and a hands-on understanding of how to apply them before moving on to the more advanced material that follows. Each chapter presents you with easy-to-follow descriptions illustrated by graphics, formulas, and plenty of solved examples. Before you know it, you'll learn to apply statistical reasoning and statistical techniques, from basic concepts of probability and hypothesis testing to multivariate analysis. Organized into four distinct sections, Statistics in a Nutshell offers you: Introductory material: Different ways to think about statistics Basic concepts of measurement and probability theory Data management for statistical analysis Research design and experimental design How to critique statistics presented by others Basic inferential statistics: Basic concepts of inferential statistics The concept of correlation, when it is and is not an appropriate measure of association Dichotomous and categorical data The distinction between parametric and nonparametric statistics Advanced inferential techniques: The General Linear Model Analysis of Variance (ANOVA) and MANOVA Multiple linear regression Specialized techniques: Business and quality improvement statistics Medical and public health statistics Educational and psychological statistics Unlike many introductory books on the subject, Statistics in a Nutshell doesn't omit important material in an effort to dumb it down. And this book is far more practical than most college texts, which tend to over-emphasize calculation without teaching you when and how to apply different statistical tests. With Statistics in a Nutshell, you learn how to perform most common statistical analyses, and understand statistical techniques presented in research articles. If you need to know how to use a wide range of statistical techniques without getting in over your head, this is the book you want.
Until the late nineteenth-century, the most common form of local government in rural England and the British Empire was administration by amateur justices of the peace: the sessions system. Petty Justice uses an unusually well-documented example of the colonial sessions system in Loyalist New Brunswick to examine the role of justices of the peace and other front-line low law officials like customs officers and deputy land surveyors in colonial local government. Using the rich archival resources of Charlotte County, Paul Craven discusses issues such as the impact of commercial rivalries on local administration, the role of low law officials in resolving civil and criminal disputes and keeping the peace, their management of public works, social welfare, and liquor regulation, and the efforts of grand juries, high court judges, colonial governors, and elected governments to supervise them. A concluding chapter explains the demise of the sessions system in Charlotte County in the decade of Confederation.
Psychological tests provide reliable and objective standards by which individuals can be evaluated in education and employment. Therefore accurate judgements must depend on the reliability and quality of the tests themselves. Originally published in 1986, this handbook by an internationally acknowledged expert provided an introductory and comprehensive treatment of the business of constructing good tests. Paul Kline shows how to construct a test and then to check that it is working well. Covering most kinds of tests, including computer presented tests of the time, Rasch scaling and tailored testing, this title offers: a clear introduction to this complex field; a glossary of specialist terms; an explanation of the objective of reliability; step-by-step guidance through the statistical procedures; a description of the techniques used in constructing and standardizing tests; guidelines with examples for writing the test items; computer programs for many of the techniques. Although the computer testing will inevitably have moved on, students on courses in occupational, educational and clinical psychology, as well as in psychological testing itself, would still find this a valuable source of information, guidance and clear explanation.
This research monograph provides a synthesis of a number of statistical tests and measures, which, at first consideration, appear disjoint and unrelated. Numerous comparisons of permutation and classical statistical methods are presented, and the two methods are compared via probability values and, where appropriate, measures of effect size. Permutation statistical methods, compared to classical statistical methods, do not rely on theoretical distributions, avoid the usual assumptions of normality and homogeneity of variance, and depend only on the data at hand. This text takes a unique approach to explaining statistics by integrating a large variety of statistical methods, and establishing the rigor of a topic that to many may seem to be a nascent field in statistics. This topic is new in that it took modern computing power to make permutation methods available to people working in the mainstream of research. lly-informed="" audience,="" and="" can="" also="" easily="" serve="" as="" textbook="" in="" graduate="" course="" departments="" such="" statistics,="" psychology,="" or="" biology.="" particular,="" the="" audience="" for="" book="" is="" teachers="" of="" practicing="" statisticians,="" applied="" quantitative="" students="" fields="" medical="" research,="" epidemiology,="" public="" health,="" biology.
Three geographically targeted volumes comprised in the Cooperative Strategies series the most ambitious effort to date to explore the extent, nature, operations, and environment of cross-border cooperative linkages in North American, European, and Asian Pacific regions. The scholars who contributed to the Cooperative Strategies series include top experts in international strategy and management. Consolidating cutting-edge scholarship and forecasting of future trends, they focus on a wide variety of new cooperative business arrangements and offer the most up-to-date assessment of them. They present the most current research on topics such as: advances in theories of cooperative strategies; the formation of cooperative alliances; the dynamics of partner relationships; and the strategy and performance of cooperative alliances. Blending conceptual insights with empirical analyses, the contributors highlight commonalities and differences across national, cultural, and trade zones. The chapters in this volume are anchored in a wide set of theoretical approaches, conceptual frameworks, and models, illustrating how rich the area of cooperative strategies is for scholarly inquiry. The Cooperative Strategies Series represents an invaluable resource for serious academic study and for business practitioners who wish to improve not only their understanding but also the performances of their joint ventures and alliances.
Paul Kline's latest book provides a readable modern account of the psychometric view of intelligence. It explains factor analysis and the construction of intelligence tests, and shows how the resulting factors provide a picture of human abilities. Written to be clear and concise it none the less provides a rigorous account of the psychometric view of intelligence.
An updated edition of a classic text on applying statistical analyses to the social sciences, with reviews, new chapters, an expanded set of post-hoc analyses, and information on computing in Excel and SPSS Now in its second edition,Statistical Applications for the Behavioral and Social Sciences has been revised and updated and continues to offer an essential guide to the conceptual foundations of statistical analyses (particularly inferential statistics), placing an emphasis on connecting statistical tools with appropriate research contexts. Designed to be accessible, the text contains an applications-oriented, step-by-step presentation of the statistical theories and formulas most often used by the social sciences. The revised text also includes an entire chapter on the basic concepts in research, presenting an overall context for all the book's statistical theories and formulas. The authors cover descriptive statistics and z scores, the theoretical underpinnings of inferential statistics, z and t tests, power analysis, one/two-way and repeated-measures ANOVA, linear correlation and regression, as well as chi-square and other nonparametric tests. The second edition also includes a new chapter on basic probability theory. This important resource: Contains information regarding the use of statistical software packages; both Excel and SPSS Offers four strategically positioned and accumulating reviews, each containing a set of research-oriented diagnostic questions designed to help students determine which tests are applicable to which research scenarios Incorporates additional statistical information on follow-up analyses such as post-hoc tests and effect sizes Includes a series of sidebar discussions dispersed throughout the text that address, among other topics, the recent and growing controversy regarding the failed reproducibility of published findings in the social sciences Puts renewed emphasis on presentation of data and findings using the APA format Includes supplementary material consisting of a set of "kick-start" quizzes designed to get students quickly back up to speed at the start of an instructional period, and a complete set of ready-to-use PowerPoint slides for in-class use Written for students in areas such as psychology, sociology, criminology, political science, public health, and others, Statistical Applications for the Behavioral and Social Sciences, Second Edition continues to provide the information needed to understand the foundations of statistical analyses as relevant to the behavioral and social sciences.
This comprehensive, yet accessible, guide to enterprise risk management for financial institutions contains all the tools needed to build and maintain an ERM framework. It discusses the internal and external contexts with which risk management must be carried out, and it covers a range of qualitative and quantitative techniques that can be used to identify, model and measure risks. This new edition has been thoroughly updated to reflect new legislation and the creation of the Financial Conduct Authority and the Prudential Regulation Authority. It includes new content on Bayesian networks, expanded coverage of Basel III, a revised treatment of operational risk and a fully revised index. Over 100 diagrams are used to illustrate the range of approaches available, and risk management issues are highlighted with numerous case studies. This book also forms part of the core reading for the UK actuarial profession's specialist technical examination in enterprise risk management, ST9.
The focus of this book is on the birth and historical development of permutation statistical methods from the early 1920s to the near present. Beginning with the seminal contributions of R.A. Fisher, E.J.G. Pitman, and others in the 1920s and 1930s, permutation statistical methods were initially introduced to validate the assumptions of classical statistical methods. Permutation methods have advantages over classical methods in that they are optimal for small data sets and non-random samples, are data-dependent, and are free of distributional assumptions. Permutation probability values may be exact, or estimated via moment- or resampling-approximation procedures. Because permutation methods are inherently computationally-intensive, the evolution of computers and computing technology that made modern permutation methods possible accompanies the historical narrative. Permutation analogs of many well-known statistical tests are presented in a historical context, including multiple correlation and regression, analysis of variance, contingency table analysis, and measures of association and agreement. A non-mathematical approach makes the text accessible to readers of all levels.
Unleash the power of Python for your data analysis projects with For Dummies! Python is the preferred programming language for data scientists and combines the best features of Matlab, Mathematica, and R into libraries specific to data analysis and visualization. Python for Data Science For Dummies shows you how to take advantage of Python programming to acquire, organize, process, and analyze large amounts of information and use basic statistics concepts to identify trends and patterns. You’ll get familiar with the Python development environment, manipulate data, design compelling visualizations, and solve scientific computing challenges as you work your way through this user-friendly guide. Covers the fundamentals of Python data analysis programming and statistics to help you build a solid foundation in data science concepts like probability, random distributions, hypothesis testing, and regression models Explains objects, functions, modules, and libraries and their role in data analysis Walks you through some of the most widely-used libraries, including NumPy, SciPy, BeautifulSoup, Pandas, and MatPlobLib Whether you’re new to data analysis or just new to Python, Python for Data Science For Dummies is your practical guide to getting a grip on data overload and doing interesting things with the oodles of information you uncover.
Statistical inference is the foundation on which much of statistical practice is built. The book covers the topic at a level suitable for students and professionals who need to understand these foundations.
This book provides the most comprehensive treatment of the theoretical concepts and modelling techniques of quantitative risk management. Whether you are a financial risk analyst, actuary, regulator or student of quantitative finance, Quantitative Risk Management gives you the practical tools you need to solve real-world problems. Describing the latest advances in the field, Quantitative Risk Management covers the methods for market, credit and operational risk modelling. It places standard industry approaches on a more formal footing and explores key concepts such as loss distributions, risk measures and risk aggregation and allocation principles. The book's methodology draws on diverse quantitative disciplines, from mathematical finance and statistics to econometrics and actuarial mathematics. A primary theme throughout is the need to satisfactorily address extreme outcomes and the dependence of key risk drivers. Proven in the classroom, the book also covers advanced topics like credit derivatives. Fully revised and expanded to reflect developments in the field since the financial crisis Features shorter chapters to facilitate teaching and learning Provides enhanced coverage of Solvency II and insurance risk management and extended treatment of credit risk, including counterparty credit risk and CDO pricing Includes a new chapter on market risk and new material on risk measures and risk aggregation
Award-winning psychology writer Annie Paul delivers a scathing exposé on the history and effects of personality tests. Millions of people worldwide take personality tests each year to direct their education, to decide on a career, to determine if they'll be hired, to join the armed forces, and to settle legal disputes. Yet, according to award-winning psychology writer Annie Murphy Paul, the sheer number of tests administered obscures a simple fact: they don't work. Most personality tests are seriously flawed, and sometimes unequivocally wrong. They fail the field's own standards of validity and reliability. They ask intrusive questions. They produce descriptions of people that are nothing like human beings as they actually are: complicated, contradictory, changeable across time and place. The Cult Of Personality Testing documents, for the first time, the disturbing consequences of these tests. Children are being labeled in limiting ways. Businesses and the government are wasting hundreds of millions of dollars every year, only to make ill-informed decisions about hiring and firing. Job seekers are having their privacy invaded and their rights trampled, and our judicial system is being undermined by faulty evidence. Paul's eye-opening chronicle reveals the fascinating history behind a lucrative and largely unregulated business. Captivating, insightful, and sometimes shocking, The Cult Of Personality Testing offers an exhilarating trip into the human mind and heart.
The first full analysis of the latest advances in managing credit risk. "Against a backdrop of radical industry evolution, the authors of Managing Credit Risk: The Next Great Financial Challenge provide a concise and practical overview of these dramatic market and technical developments in a book which is destined to become a standard reference in the field." -Thomas C. Wilson, Partner, McKinsey & Company, Inc. "Managing Credit Risk is an outstanding intellectual achievement. The authors have provided investors a comprehensive view of the state of credit analysis at the end of the millennium." -Martin S. Fridson, Financial Analysts Journal. "This book provides a comprehensive review of credit risk management that should be compulsory reading for not only those who are responsible for such risk but also for financial analysts and investors. An important addition to a significant but neglected subject." -B.J. Ranson, Senior Vice-President, Portfolio Management, Bank of Montreal. The phenomenal growth of the credit markets has spawned a powerful array of new instruments for managing credit risk, but until now there has been no single source of information and commentary on them. In Managing Credit Risk, three highly regarded professionals in the field have-for the first time-gathered state-of-the-art information on the tools, techniques, and vehicles available today for managing credit risk. Throughout the book they emphasize the actual practice of managing credit risk, and draw on the experience of leading experts who have successfully implemented credit risk solutions. Starting with a lucid analysis of recent sweeping changes in the U.S. and global financial markets, this comprehensive resource documents the credit explosion and its remarkable opportunities-as well as its potentially devastating dangers. Analyzing the problems that have occurred during its growth period-S&L failures, business failures, bond and loan defaults, derivatives debacles-and the solutions that have enabled the credit market to continue expanding, Managing Credit Risk examines the major players and institutional settings for credit risk, including banks, insurance companies, pension funds, exchanges, clearinghouses, and rating agencies. By carefully delineating the different perspectives of each of these groups with respect to credit risk, this unique resource offers a comprehensive guide to the rapidly changing marketplace for credit products. Managing Credit Risk describes all the major credit risk management tools with regard to their strengths and weaknesses, their fitness to specific financial situations, and their effectiveness. The instruments covered in each of these detailed sections include: credit risk models based on accounting data and market values; models based on stock price; consumer finance models; models for small business; models for real estate, emerging market corporations, and financial institutions; country risk models; and more. There is an important analysis of default results on corporate bonds and loans, and credit rating migration. In all cases, the authors emphasize that success will go to those firms that employ the right tools and create the right kind of risk culture within their organizations. A strong concluding chapter integrates emerging trends in the financial markets with the new methods in the context of the overall credit environment. Concise, authoritative, and lucidly written, Managing Credit Risk is essential reading for bankers, regulators, and financial market professionals who face the great new challenges-and promising rewards-of credit risk management.
The Measurement of Health and Health Status: Concepts, Methods and Applications from a Multidisciplinary Perspective presents a unifying perspective on how to select the best measurement framework for any situation. Serving as a one-stop shop that unifies material currently available in various locations, this book illuminates the intuition behind each method, explaining how each method has special purposes, what developments are occurring, and how new combinations among methods might be relevant to specific situations. It especially emphasizes the measurement of health and health states (quality-of-life), giving significant attention to newly developed methods. The book introduces technically complex, new methods for both introductory and technically-proficient readers. Assumes that the best measure depends entirely on the situation Covers preference-based methods, classical test theory, and item response theory Features illustrations and animations drawn from diverse fields and disciplines
THIS TEXTBOOK IS A COMPREHENSIVE USER FRIENDLY AND EASY TO READ RESOURCE ON BIOSTATISTICS AND RESEARCH METHODOLOGY. IT IS MEANT FOR UNDERGRADUATE AND POSTGRADUATE MEDICAL STUDENTS AND ALLIED BIOMEDICAL SCIENCES. HEALTH RESEARCHERS, RESEARCH SUPERVISORS AND FACULTY MEMBERS MAY FIND IT USEFUL AS A REFERENCE BOOK
Each chapter of this book covers specific topics in statistical analysis, such as robust alternatives to t-tests or how to develop a questionnaire. They also address particular questions on these topics, which are commonly asked by human-computer interaction (HCI) researchers when planning or completing the analysis of their data. The book presents the current best practice in statistics, drawing on the state-of-the-art literature that is rarely presented in HCI. This is achieved by providing strong arguments that support good statistical analysis without relying on mathematical explanations. It additionally offers some philosophical underpinnings for statistics, so that readers can see how statistics fit with experimental design and the fundamental goal of discovering new HCI knowledge.
Since publication in its first edition the Handbook of Psychological Testing has become the standard text for organisational and educational psychologists. It offers the only comprehensicve, modern and clear account of the whole of the field of psychometrics. It covers psychometric theory, the different kinds of psychological test, applied psychological testing, and the evaluation of the best published psychological tests. It is outstanding for its detailed and complete coverage of the field, its clarity (even for the non-mathematical) and its emphasis on the practical application of psychometric theory in psychology and education, as well as in vocational, occupational and clinical fields. For this second edition the Handbook has been extensively revised and updated to include the latest research and thinking in the field. Unlike other work in this area, it challenges the scientific rigour of conventional psychometrics and identifies groundbreaking new ways forward.
This study develops a new indicator for national and global sustainability. The main components of the EIIW-vita indicator are: the share of renewable energy, the genuine savings rate and the relative "green export" position of the respective countries; it is in line with OECD requirements on composite indicators. As green exports are related to technological progress and environmental-friendly products, there is also a Schumpeterian perspective of this indicator. An extended version furthermore looks at water productivity. The analysis highlights the BRIICS countries as well as the US, Germany, France, Spain, Italy, the UK and Japan. Moreover the special challenges and dynamics of ASEAN countries and Asia are discussed. The book derives key implications for economic and environmental policy and shows that the new global sustainability indicator is not only relevant for green progress, but also useful as a signal for international investors. The construction of the EIIW-vita global sustainability indicator is such that investors, citizens and governments can easily interpret the results. Correlation analysis of the new sustainability indicator with the human development index indicates complementarity, so that a new hybrid superindicator can be constructed. Sustainability rhetoric dominates environmental policy. This fresh assessment of key "pillars" of sustainable economic performance and growth is a valuable contribution to greening the economy, the leitmotiv of the latest Rio Earth Summit. The book places the discussion of sustainability on solid data. The rather surprising results of its new sustainability index should make policy makers rethink their environmental and economic strategies. Prof. Dr. Peter Bartelmus Columbia University, New York Many people put the economy first when sustainability concerns are raised, while environmental indicators are often developed without a sense of socio-economic performance. This important new book bridges the gap. It sheds light on crucial indicators such as renewable energies, exporting green goods and services, genuine savings, and water productivity. And it helps to observe the impressive changes at a global scale and in countries such as China. A must read for all experts interested in those issues. Prof. Dr. Raimund Bleischwitz University College London
The primary purpose of this textbook is to introduce the reader to a wide variety of elementary permutation statistical methods. Permutation methods are optimal for small data sets and non-random samples, and are free of distributional assumptions. The book follows the conventional structure of most introductory books on statistical methods, and features chapters on central tendency and variability, one-sample tests, two-sample tests, matched-pairs tests, one-way fully-randomized analysis of variance, one-way randomized-blocks analysis of variance, simple regression and correlation, and the analysis of contingency tables. In addition, it introduces and describes a comparatively new permutation-based, chance-corrected measure of effect size. Because permutation tests and measures are distribution-free, do not assume normality, and do not rely on squared deviations among sample values, they are currently being applied in a wide variety of disciplines. This book presents permutation alternatives to existing classical statistics, and is intended as a textbook for undergraduate statistics courses or graduate courses in the natural, social, and physical sciences, while assuming only an elementary grasp of statistics.
The recent turbulence in the stock market has brought into question the way, and prices at which, shares are traded, and how the market effectively values companies. It has also raised public concern as to the way by which dealers and investors take advantage of changes in market prices. A number of high profile criminal prosecutions of insider dealing and market abuse and the frequent claims of other instances, combined with the changes in regulations resulting in a more aggressive and proactive stance by the various regulators, have brought the issue under the spotlight. This book discusses what makes stock market efficiency so important for the economy, looks at the theory and issues that underpin market abuse and why an offence often dismissed as a victimless crime is punished so severely. It explores the impact of perception and other factors that distort the market and outlines the extent of abuse. Regulators, lawyers, company officials, investigators, professional advisers and of course investors, both professional and otherwise will find this a helpful guide to the underlying elements of fraud and market manipulation.
Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences A practical guide to the use of basic principles of experimental design and statistical analysis in pharmacology Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences provides clear instructions on applying statistical analysis techniques to pharmacological data. Written by an experimental pharmacologist with decades of experience teaching statistics and designing preclinical experiments, this reader-friendly volume explains the variety of statistical tests that researchers require to analyze data and draw correct conclusions. Detailed, yet accessible, chapters explain how to determine the appropriate statistical tool for a particular type of data, run the statistical test, and analyze and interpret the results. By first introducing basic principles of experimental design and statistical analysis, the author then guides readers through descriptive and inferential statistics, analysis of variance, correlation and regression analysis, general linear modelling, and more. Lastly, throughout the textbook are numerous examples from molecular, cellular, in vitro, and in vivo pharmacology which highlight the importance of rigorous statistical analysis in real-world pharmacological and biomedical research. This textbook also: Describes the rigorous statistical approach needed for publication in scientific journals Covers a wide range of statistical concepts and methods, such as standard normal distribution, data confidence intervals, and post hoc and a priori analysis Discusses practical aspects of data collection, identification, and presentation Features images of the output from common statistical packages, including GraphPad Prism, Invivo Stat, MiniTab and SPSS Experimental Design and Statistical Analysis for Pharmacology and the Biomedical Sciences is an invaluable reference and guide for undergraduate and graduate students, post-doctoral researchers, and lecturers in pharmacology and allied subjects in the life sciences.
This book serves as a roadmap for the development and application of patient-reported outcome (PRO) measures, supporting beginners through to experts, as a practical guide. To elucidate on key concepts in the book, examples from clinical research in hyperhidrosis and health-related quality of life and medicines clinical development context, are used. Health-related quality of life represents one of the most commonly measured PROs in both routine clinical practice and research. The book demonstrates the importance of PROs to patients with chronic disease and how such outcomes can assist clinicians in managing patients and monitoring their response to treatment in terms of both symptoms and impacts. This book will benefit readers as a single-source practical guide on the development of modern PRO measures and may also serve as a blueprint for the conceptualization and planning of evidence generation related to PROs in various settings. Ideas and suggestions on how to navigate recent developments shaping the field of PRO measurement are also offered.
Probability Methods for Cost Uncertainty Analysis: A Systems Engineering Perspective, Second Edition gives you a thorough grounding in the analytical methods needed for modeling and measuring uncertainty in the cost of engineering systems. This includes the treatment of correlation between the cost of system elements, how to present the analysis to
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