By the end of this book, the reader will understand: the difficulties of finding a needle in a haystack; creative solutions to address the problem; unique ways of engineering features and solving the problem of the lack of data (e.g. synthetic data). Additionally, the reader will be able to: avoid mistakes resulting from a lack of understanding; search for appropriate methods of feature engineering; locate the relevant technological solutions within the general context of decision-making.

Helps readers to transition from traditional statistics to modern data science Reviews the pros and cons of open source and commercial software packages, and their proper applications in specific situations. Explores data using dynamic methods rather than counting on dichotomous thinking. Considers alternate models using ensemble models and model comparison rather than fixing a preconceived hypothesis/model on a single method. Shows how to find the hidden pattern in the data by dynamic visualization rather than over-relying on numeric results.

-Includes simplified statistical contents and step by step guide on how to apply the statistical concepts by perform analysis using SPSS together with interpretation of the statistical analysis output. -Provides a wide range of data sets to be used for examples and illustrations. -Designed to be accessible to readers with varied backgrounds.

This book provides in-depth coverage of Probability & Statistics 2 for Cambridge International A Level Mathematics 9709, for examination from 2020 onwards. With a clear focus on mathematics in life and work, this text builds the key mathematical skills and knowledge that will open up a wide range of careers and further study. Exam Board: Cambridge Assessment International Education First teaching: 2018 First examination: 2020 This student book is part of a series of nine books covering the complete syllabus for Cambridge International AS and A Level Mathematics (9709) and Further Mathematics (9231), for first teaching from September 2018 and examination from 2020. This title is endorsed by Cambridge Assessment International Education. Written by expert authors, this Student Book: * covers the complete content of Probability & Statistics 2 with clear references to what you will learn at the start of each chapter, and coverage that clearly and directly matches the Cambridge syllabus * sets mathematics in real-world contexts that emphasise practical applications and career paths, with inspiring case studies and discussion activities that showcase how mathematics is relevant to different roles * develops the key A Level mathematical skills of mathematical modelling, problem-solving and communication through dedicated questions and teaching * helps you master mathematics with varied practice to develop understanding, exam-style questions to test comprehension, and selected Cambridge past paper questions to help prepare for examination * gives you control of your learning with prior knowledge checks to assess readiness and end-of-chapter summaries that test understanding * supports you through the course with detailed explanations, clear worked examples and plenty of graduated practice on each topic with full workings shown for each answer * provides clear progression from IGCSE (R) Mathematics and develops confident, independent and reflective mathematicians through extension questions and group discussions * supports mathematical communication and literacy with key terms for each topic explained and supported by a comprehensive glossary.

Handbook of Spatial Epidemiology explains how to model epidemiological problems and improve inference about disease etiology from a geographical perspective. Top epidemiologists, geographers, and statisticians share interdisciplinary viewpoints on analyzing spatial data and space-time variations in disease incidences. These analyses can provide important information that leads to better decision making in public health. The first part of the book addresses general issues related to epidemiology, GIS, environmental studies, clustering, and ecological analysis. The second part presents basic statistical methods used in spatial epidemiology, including fundamental likelihood principles, Bayesian methods, and testing and nonparametric approaches. With a focus on special methods, the third part describes geostatistical models, splines, quantile regression, focused clustering, mixtures, multivariate methods, and much more. The final part examines special problems and application areas, such as residential history analysis, segregation, health services research, health surveys, infectious disease, veterinary topics, and health surveillance and clustering. Spatial epidemiology, also known as disease mapping, studies the geographical or spatial distribution of health outcomes. This handbook offers a wide-ranging overview of state-of-the-art approaches to determine the relationships between health and various risk factors, empowering researchers and policy makers to tackle public health problems.

Designed for students training to become biostatisticians as well as practicing biostatisticians, Inference Principles for Biostatisticians presents the theoretical and conceptual foundations of biostatistics. It covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field. Drawing on his extensive experience teaching graduate-level biostatistics courses and working in the pharmaceutical industry, the author explains the main principles of statistical inference with many examples and exercises. Extended examples illustrate key concepts in depth using a specific biostatistical context. In addition, the author uses simulation to reinforce the repeated sampling interpretation of numerous statistical concepts. Reducing the computational complexities, he provides simple R functions for conducting simulation studies. This text gives graduate students with diverse backgrounds across the health, medical, social, and mathematical sciences a solid, unified foundation in the principles of statistical inference. This groundwork will lead students to develop a thorough understanding of biostatistical methodology.

Score your highest in biostatistics Biostatistics is a required course for students of medicine, epidemiology, forestry, agriculture, bioinformatics, and public health. In years past this course has been mainly a graduate-level requirement; however its application is growing and course offerings at the undergraduate level are exploding. Biostatistics For Dummies is an excellent resource for those taking a course, as well as for those in need of a handy reference to this complex material. Biostatisticians analysts of biological data are charged with finding answers to some of the world's most pressing health questions: how safe or effective are drugs hitting the market today? What causes autism? What are the risk factors for cardiovascular disease? Are those risk factors different for men and women or different ethnic groups? Biostatistics For Dummies examines these and other questions associated with the study of biostatistics. * Provides plain-English explanations of techniques and clinical examples to help * Serves as an excellent course supplement for those struggling with the complexities of the biostatistics * Tracks to a typical, introductory biostatistics course Biostatistics For Dummies is an excellent resource for anyone looking to succeed in this difficult course.

Strange happenstances and chance encounters have puzzled us for centuries. This fun and fascinating book takes readers on a journey through the mathematics behind coincidences both famous and never-before-examined. From peculiar patterns in geometry and calculus to the famous Waring Problem, and other astonishing numerical curiosities, The Call of Coincidence begins by examining the mathematical properties that underpin everything there is. Next, author Owen O'Shea - along with fictional guides Charlie Chance and the enigmatic Dr. Moogle - reveals surprising connections and correlations throughout history, including numerical coincidences behind the reign of King Richard III, the sinking of the SS Edmund Fitzgerald, the 1996 FIFA World Cup, and much, much more. By investigating the properties, puzzles, and problems within, you will gain a newfound appreciation for the beautiful simplicity of mathematics in its many forms. Featuring surprising trivia gems alongside serious questions like why there is something rather than nothing, readers will be enriched by this exploration of remarkable number coincidences and the mathematics that make them possible - and probable.

First Published in 1967. Routledge is an imprint of Taylor & Francis, an informa company.

Provides a concise and intuitive overview of the role of statistics in surgical practice. Uses no mathematics but includes narrative descriptions of statistical concepts. Includes topics of specific interest to surgeons. Provides real surgical examples to illustrate applications.

Features Includes insight questions to support class discussion Written in broadly non-mathematical terms, designed to be accessible to any level of student or practicing AP to read, understand, and implement the concepts Each section introduces the ideas through real-life case studies

Guides the reader into the mysteries of water. Provides a state-of-the-art overview in computer simulations and experiments on water. Brings together leading scientists in the field of water.

This book developed from a course given by the author to undergraduate and postgraduate students. It takes up Matrix Theory, Antenna Theory, and Probability Theory in detail. The first chapter on matrix theory discusses in reasonable depth the theory of Lie Algebras leading upto Cartan's Classification Theory. It also discusses some basic elements of Functional Analysis and Operator Theory in infinite dimensional Banach and Hilbert spaces. The second chapter discusses Basic Probability Theory and the topics discussed find applications to Stochastic Filtering Theory for differential equations driven by white Gaussian noise. The third chapter is on Antenna Theory with a focus on Modern Quantum Antenna Theory. The book will be a valuable resource to students and early career researchers in the field of Mathametical Physics.

Making decisions is a ubiquitous mental activity in our private and professional or public lives. It entails choosing one course of action from an available shortlist of options. Statistics for Making Decisions places decision making at the centre of statistical inference, proposing its theory as a new paradigm for statistical practice. The analysis in this paradigm is earnest about prior information and the consequences of the various kinds of errors that may be committed. Its conclusion is a course of action tailored to the perspective of the specific client or sponsor of the analysis. The author's intention is a wholesale replacement of hypothesis testing, indicting it with the argument that it has no means of incorporating the consequences of errors which self-evidently matter to the client. The volume appeals to the analyst who deals with the simplest statistical problems of comparing two samples (which one has a greater mean or variance), or deciding whether a parameter is positive or negative. It combines highlighting the deficiencies of hypothesis testing with promoting a principled solution based on the idea of a currency for error, of which we want to spend as little as possible. This is implemented by selecting the option for which the expected loss is smallest (the Bayes rule). The price to pay is the need for a more detailed description of the options, and eliciting and quantifying the consequences (ramifications) of the errors. This is what our clients do informally and often inexpertly after receiving outputs of the analysis in an established format, such as the verdict of a hypothesis test or an estimate and its standard error. As a scientific discipline and profession, statistics has a potential to do this much better and deliver to the client a more complete and more relevant product. Nicholas T. Longford is a senior statistician at Imperial College, London, specialising in statistical methods for neonatal medicine. His interests include causal analysis of observational studies, decision theory, and the contest of modelling and design in data analysis. His longer-term appointments in the past include Educational Testing Service, Princeton, NJ, USA, de Montfort University, Leicester, England, and directorship of SNTL, a statistics research and consulting company. He is the author of over 100 journal articles and six other monographs on a variety of topics in applied statistics.

Introduces the GUHA method of mechanizing hypothesis formation as a data mining tool. Presents examples of data mining with enhanced association rules, histograms, contingency tables and action rules. Provides examples of data mining for exception rules and examples of subgroups discovery. Outlines possibilities of GUHA in business intelligence and big data. Overviews related theoretical results and challenges related to mechanizing hypothesis formation.

Reviews the literature of the Moth-Flame Optimization algorithm; Provides an in-depth analysis of equations, mathematical models, and mechanisms of the Moth-Flame Optimization algorithm; Proposes different variants of the Moth-Flame Optimization algorithm to solve binary, multi-objective, noisy, dynamic, and combinatorial optimization problems; Demonstrates how to design, develop, and test different hybrids of Moth-Flame Optimization algorithm; Introduces several applications areas of the Moth-Flame Optimization algorithm focusing in sustainability.

Explanatory Model Analysis Explore, Explain and Examine Predictive Models is a set of methods and tools designed to build better predictive models and to monitor their behaviour in a changing environment. Today, the true bottleneck in predictive modelling is neither the lack of data, nor the lack of computational power, nor inadequate algorithms, nor the lack of flexible models. It is the lack of tools for model exploration (extraction of relationships learned by the model), model explanation (understanding the key factors influencing model decisions) and model examination (identification of model weaknesses and evaluation of model's performance). This book presents a collection of model agnostic methods that may be used for any black-box model together with real-world applications to classification and regression problems.

Association does not imply causation, yet some causal conclusions are firmly established based on associations found in observational studies. How does that happen? A study has two evidence factors if it provides two statistically independent tests of one causal hypothesis, susceptible to different biases. Two evidence factors can jointly provide quantifiably stronger evidence than either factor can provide on its own. The first book about evidence factors. Examples are drawn from epidemiology, economics, medical research and other fields. Data from these examples is available in a companion R package that reproduces many of the analyses. Self-contained, presenting needed background from causal inference, statistics and mathematics. Part 1 of the book presents concepts, methods and applications using limited mathematics. The theory of evidence factors is presented in a separate, second part of the book. Mathematics required for the theory is presented from the beginning.

This book describes the fundamental and theoretical concepts of optimization algorithms in a systematic manner, along with their potential applications and implementation strategies in mining engineering. It explains basics of systems engineering, linear programming, and integer linear programming, transportation and assignment algorithms, network analysis, dynamic programming, queuing theory and their applications to mine systems. Reliability analysis of mine systems, inventory management in mines, and applications of non-linear optimization in mines are discussed as well. All the optimization algorithms are explained with suitable examples and numerical problems in each of the chapters. Features include: * Integrates operations research, reliability, and novel computerized technologies in single volume, with a modern vision of continuous improvement of mining systems. * Systematically reviews optimization methods and algorithms applied to mining systems including reliability analysis. * Gives out software-based solutions such as MATLAB (R), AMPL, LINDO for the optimization problems. * All discussed algorithms are supported by examples in each chapter. * Includes case studies for performance improvement of the mine systems. This book is aimed primarily at professionals, graduate students, and researchers in mining engineering.

Describes classical, basic sampling designs for spatial survey, as well as recently developed, advanced sampling designs and estimators Presents probability sampling designs for estimating parameters for a (sub)population, as well as non-probability sampling designs for mapping Gives comprehensive overview of model-assisted estimators Illustrates sampling designs with surveys of soil organic carbon, above-ground biomass, air temperature, opium poppy Explains integration of wall-to-wall data sets (e.g. remote sensing images) and sample data Data and R code available on github Exercises added making the book suitable as a textbook for students

Actuaries must pass exams, but more than that: they must put knowledge into practice. This coherent book supports the Society of Actuaries' short-term actuarial mathematics syllabus while emphasizing the concepts and practical application of nonlife actuarial models. A class-tested textbook for undergraduate courses in actuarial science, it is also ideal for those approaching their professional exams. Key topics covered include loss modelling, risk and ruin theory, credibility theory and applications, and empirical implementation of loss models. Revised and updated to reflect curriculum changes, this second edition includes two brand new chapters on loss reserving and ratemaking. R replaces Excel as the computation tool used throughout - the featured R code is available on the book's webpage, as are lecture slides. Numerous examples and exercises are provided, with many questions adapted from past Society of Actuaries exams.

The methodological needs of environmental studies are unique in the breadth of research questions that can be posed, calling for a textbook that covers a broad swath of approaches to conducting research with potentially many different kinds of evidence. Fully updated to address new developments such as the effects of the internet, recent trends in the use of computers, remote sensing, and large data sets, this new edition of Research Methods for Environmental Studies is written specifically for social science-based research into the environment. This revised edition contains new chapters on coding, focus groups, and an extended treatment of hypothesis testing. The textbook covers the best-practice research methods most used to study the environment and its connections to societal and economic activities and objectives. Over five key parts, Kanazawa introduces quantitative and qualitative approaches, mixed methods, and the special requirements of interdisciplinary research, emphasizing that methodological practice should be tailored to the specific needs of the project. Within these parts, detailed coverage is provided on key topics including the identification of a research project, hypothesis testing, spatial analysis, the case study method, ethnographic approaches, discourse analysis, mixed methods, survey and interview techniques, focus groups, and ethical issues in environmental research. Drawing on a variety of extended and updated examples to encourage problem-based learning and fully addressing the challenges associated with interdisciplinary investigation, this book will be an essential resource for students embarking on courses exploring research methods in environmental studies.

The International Biometric Society (IBS) was formed at the First International Biometric Conference at Woods Hole on September 6, 1947. The History of the International Biometric Society presents a deep dive into the voluminous archival records, with primary focus on IBS's first fifty years. It contains numerous photos and extracts from the archival materials, and features many photos of important leaders who served IBS across the decades. Features: Describes events leading up to and at Woods Hole on September 6, 1947 that led to the formation of IBS Outlines key markers that shaped IBS after the 1947 formation through to the modern day Describes the regional and national group structure, and the formation of regions and national groups Describes events surrounding the key scientific journal of IBS, Biometrics, including the transfer of ownership to IBS, content, editors, policies, management, and importance Describes the other key IBS publications - Biometric Bulletin, Journal of Agricultural Biological and Environmental Statistics, and regional publications Provides details of International Biometric Conferences and key early symposia Describes IBS constitution and by-laws processes, and the evolution of business arrangements Provides a record of international officers, including regional presidents, national group secretaries, journal editors, and the locations of meetings Includes a gallery of international Presidents, and a gallery of Secretaries and Treasurers The History of the International Biometric Society will appeal to anyone interested in the activities of our statistical and biometrical forebearers. The focus is on issues and events that engaged the attention of the officers of IBS. Some of these records are riveting, some entertaining, some intriguing, and some colorful. Some of the issues covered were difficult to handle, but even these often resulted in changes that benefited IBS.

The International Biometric Society (IBS) was formed at the First International Biometric Conference at Woods Hole on September 6, 1947. The History of the International Biometric Society presents a deep dive into the voluminous archival records, with primary focus on IBS's first fifty years. It contains numerous photos and extracts from the archival materials, and features many photos of important leaders who served IBS across the decades. Features: Describes events leading up to and at Woods Hole on September 6, 1947 that led to the formation of IBS Outlines key markers that shaped IBS after the 1947 formation through to the modern day Describes the regional and national group structure, and the formation of regions and national groups Describes events surrounding the key scientific journal of IBS, Biometrics, including the transfer of ownership to IBS, content, editors, policies, management, and importance Describes the other key IBS publications - Biometric Bulletin, Journal of Agricultural Biological and Environmental Statistics, and regional publications Provides details of International Biometric Conferences and key early symposia Describes IBS constitution and by-laws processes, and the evolution of business arrangements Provides a record of international officers, including regional presidents, national group secretaries, journal editors, and the locations of meetings Includes a gallery of international Presidents, and a gallery of Secretaries and Treasurers The History of the International Biometric Society will appeal to anyone interested in the activities of our statistical and biometrical forebearers. The focus is on issues and events that engaged the attention of the officers of IBS. Some of these records are riveting, some entertaining, some intriguing, and some colorful. Some of the issues covered were difficult to handle, but even these often resulted in changes that benefited IBS.

Features Probability models are developed from the shape of the sample empirical cumulative distribution function, (cdf) or a transformation of it. Bounds for the value of the population cumulative distribution function are obtained from the Beta distribution at each point of the empirical cdf. Bayes's theorem is developed from the properties of the screening test for a rare condition. The multinomial distribution provides an always-true model for any randomly sampled data. The model-free bootstrap method for finding the precision of a sample estimate has a model-based parallel - the Bayesian bootstrap - based on the always-true multinomial distribution. The Bayesian posterior distributions of model parameters can be obtained from the maximum likelihood analysis of the model.