Designed to offer an accessible set of case studies and analyses of ethical dilemmas in data science. This book will be suitable for technical readers in data science who want to understand diverse ethical approaches to AI.

Provides insight on building smart sustainable solutions Includes details of applying mining, learning, IOT and sensor-based techniques for sustainable computing Entails data extraction from various sources followed with pre-processing of data, and how to make effective use of extracted data for application-based research Involves practical usage of data analytic language, including R, Python, etc. for improving sustainable services offered by multi-disciplinary domains Encompasses comparison and analysis of recent technologies and trends Includes development of smart models for information gain and effective decision making with visualization

Sample Sizes for Clinical Trials, Second Edition is a practical book that assists researchers in their estimation of the sample size for clinical trials. Throughout the book there are detailed worked examples to illustrate both how to do the calculations and how to present them to colleagues or in protocols. The book also highlights some of the pitfalls in calculations as well as the key steps that lead to the final sample size calculation. Features: Comprehensive coverage of sample size calculations, including Normal, binary, ordinal, and survival outcome data Covers superiority, equivalence, non-inferiority, bioequivalence and precision objectives for both parallel group and crossover designs Highlights how trial objectives impact the study design with respect to both the derivation of sample formulae and the size of the study Motivated with examples of real-life clinical trials showing how the calculations can be applied New edition is extended with all chapters revised, some substantially, and four completely new chapters on multiplicity, cluster trials, pilot studies, and single arm trials The book is primarily aimed at researchers and practitioners of clinical trials and biostatistics, and could be used to teach a course on sample size calculations. The importance of a sample size calculation when designing a clinical trial is highlighted in the book. It enables readers to quickly find an appropriate sample size formula, with an associated worked example, complemented by tables to assist in the calculations.

Provides an introduction to statistical thinking that will help the public consume results reported in the popular media.

Builds a market simulator to back test trading algorithms Implements closed-form strategies that optimize trading signals Measures liquidity risk and stress test portfolios for fire sales Analyze algorithms’ performance controlling for common trading biases Estimates price impact models using the public trading tape

With the boom of big data and machine learning and the subsequent need for parallel processing technologies, fork-join queues are more relevant now than ever before. In this book, new estimates of the average response time in fork-join queues are proposed, which form the basis for new research opportunities. Analysis of Fork-Join Systems: Network of Queues with Precedence Constraints explores numerical approaches to estimate the average response time of fork-join queueing networks and offers never before published simple expressions for the mean response time as conjectures. Extensive experiments are included to demonstrate the remarkable accuracy of the conjectures and algorithms used in the estimation of the average response time. Graduate students, professors, and researchers in the fields of operations research, management science, industrial engineering, computer science, and electrical engineering will find this book very useful. Students, as well as researchers in both academia and industry, will also find this book of great help when looking for results related to fork-join queues

Now in its Seventh Edition, Neil J. Salkind's bestselling Statistics for People Who (Think They) Hate Statistics with new co-author Bruce B. Frey teaches an often intimidating subject with a humorous, personable, and informative approach that reduces statistics anxiety. With instruction in SPSS(R), the authors guide students through basic and advanced statistical procedures, from correlation and graph creation to analysis of variance, regression, non-parametric tests, and more. The Seventh Edition includes new real-world examples, additional coverage on multiple regression and power and effect size, and a robust interactive eBook with video tutorials and animations of key concepts. In the end, students who (think they) hate statistics will understand how to explain the results of many statistical analyses and won't be intimidated by basic statistical tasks.

.".".I know of no better book of its kind..." (Journal of the Royal Statistical Society, Vol 169 (1), January 2006)"

A revised and updated edition of this bestselling introductory textbook to statistical analysis using the leading free software package R

This new edition of a bestselling title offers a concise introduction to a broad array of statistical methods, at a level that is elementary enough to appeal to a wide range of disciplines. Step-by-step instructions help the non-statistician to fully understand the methodology. The book covers the full range of statistical techniques likely to be needed to analyse the data from research projects, including elementary material like t--tests and chi--squared tests, intermediate methods like regression and analysis of variance, and more advanced techniques like generalized linear modelling.

Includes numerous worked examples and exercises within each chapter.

Modelling Survival Data in Medical Research, Fourth Edition describes the analysis of survival data, illustrated using a wide range of examples from biomedical research. Written in a non-technical style, it concentrates on how the techniques are used in practice. Starting with standard methods for summarising survival data, Cox regression and parametric modelling, the book covers many more advanced techniques, including interval-censoring, frailty modelling, competing risks, analysis of multiple events, and dependent censoring. This new edition contains chapters on Bayesian survival analysis and use of the R software. Earlier chapters have been extensively revised and expanded to add new material on several topics. These include methods for assessing the predictive ability of a model, joint models for longitudinal and survival data, and modern methods for the analysis of interval-censored survival data. Features: Presents an accessible account of a wide range of statistical methods for analysing survival data Contains practical guidance on modelling survival data from the author's many years of experience in teaching and consultancy Shows how Bayesian methods can be used to analyse survival data Includes details on how R can be used to carry out all the methods described, with guidance on the interpretation of the resulting output Contains many real data examples and additional data sets that can be used for coursework All data sets used are available in electronic format from the publisher's website Modelling Survival Data in Medical Research, Fourth Edition is an invaluable resource for statisticians in the pharmaceutical industry and biomedical research centres, research scientists and clinicians who are analysing their own data, and students following undergraduate or postgraduate courses in survival analysis.

This textbook provides an introduction to the scientific study of sociology and other social sciences. It offers the basic tools necessary for readers to become both critical consumers and beginning producers of scientific research on society. The authors present an integrated approach to research design and empirical analyses in which researchers can develop and test causal theories. They use examples from social science research that students will find engaging and inspiring and that will help them to understand key concepts. The book makes technical materials accessible to students who might otherwise be intimidated by mathematical examples. This new text, with the addition of sociologist Steven A. Tuch to the author team, follows the successful format, approach, and pedagogical features in Paul M. Kellstedt and Guy D. Whitten's bestselling text, The Fundamentals of Political Science Research, now in its third edition. Workbooks in Stata, SPSS, and R, three of the most popular statistical analysis programs, are available as separate purchases to accompany this textbook, enabling students to connect the lessons of this book to hands-on applications of the software.

In this second edition of Counterfactuals and Causal Inference, completely revised and expanded, the essential features of the counterfactual approach to observational data analysis are presented with examples from the social, demographic, and health sciences. Alternative estimation techniques are first introduced using both the potential outcome model and causal graphs; after which, conditioning techniques, such as matching and regression, are presented from a potential outcomes perspective. For research scenarios in which important determinants of causal exposure are unobserved, alternative techniques, such as instrumental variable estimators, longitudinal methods, and estimation via causal mechanisms, are then presented. The importance of causal effect heterogeneity is stressed throughout the book, and the need for deep causal explanation via mechanisms is discussed.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Immediately implementable code, with extensive and varied illustrations of graph variants and layouts. Examples and exercises across a variety of real-life contexts including business, politics, education, social media and crime investigation. Dedicated chapter on graph visualization methods. Practical walkthroughs of common methodological uses: finding influential actors in groups, discovering hidden community structures, facilitating diverse interaction in organizations, detecting political alignment, determining what influences connection and attachment. Various downloadable data sets for use both in class and individual learning projects. Final chapter dedicated to individual or group project examples.

Self-contained chapters on the most important applications and methodologies in finance, which can easily be used for the reader’s research or as a reference for courses on empirical finance. Each chapter is reproducible in the sense that the reader can replicate every single figure, table, or number by simply copy-pasting the code we provide. A full-fledged introduction to machine learning with tidymodels based on tidy principles to show how factor selection and option pricing can benefit from Machine Learning methods. Chapter 2 on accessing & managing financial data shows how to retrieve and prepare the most important datasets in the field of financial economics: CRSP and Compustat. The chapter also contains detailed explanations of the most important data characteristics. Each chapter provides exercises that are based on established lectures and exercise classes and which are designed to help students to dig deeper. The exercises can be used for self-studying or as source of inspiration for teaching exercises.

As a result of the COVID-19 pandemic, medical statistics and public health data have become staples of newsfeeds worldwide, with infection rates, deaths, case fatality and the mysterious R figure featuring regularly. However, we don't all have the statistical background needed to translate this information into knowledge. In this lively account, Stephen Senn explains these statistical phenomena and demonstrates how statistics is essential to making rational decisions about medical care. The second edition has been thoroughly updated to cover developments of the last two decades and includes a new chapter on medical statistical challenges of COVID-19, along with additional material on infectious disease modelling and representation of women in clinical trials. Senn entertains with anecdotes, puzzles and paradoxes, while tackling big themes including: clinical trials and the development of medicines, life tables, vaccines and their risks or lack of them, smoking and lung cancer, and even the power of prayer.

Praise for the First Edition "If you ... want an up-to-date, definitive reference written by authors who have contributed much to this field, then this book is an essential addition to your library." --Journal of the American Statistical Association A COMPREHENSIVE REVIEW OF MODERN EXPERIMENTAL DESIGN Experiments: Planning, Analysis, and Optimization, Third Edition provides a complete discussion of modern experimental design for product and process improvement--the design and analysis of experiments and their applications for system optimization, robustness, and treatment comparison. While maintaining the same easy-to-follow style as the previous editions, this book continues to present an integrated system of experimental design and analysis that can be applied across various fields of research including engineering, medicine, and the physical sciences. New chapters provide modern updates on practical optimal design and computer experiments, an explanation of computer simulations as an alternative to physical experiments. Each chapter begins with a real-world example of an experiment followed by the methods required to design that type of experiment. The chapters conclude with an application of the methods to the experiment, bridging the gap between theory and practice. The authors modernize accepted methodologies while refining many cutting-edge topics including robust parameter design, analysis of non-normal data, analysis of experiments with complex aliasing, multilevel designs, minimum aberration designs, and orthogonal arrays. The third edition includes: Information on the design and analysis of computer experiments A discussion of practical optimal design of experiments An introduction to conditional main effect (CME) analysis and definitive screening designs (DSDs) New exercise problems This book includes valuable exercises and problems, allowing the reader to gauge their progress and retention of the book's subject matter as they complete each chapter. Drawing on examples from their combined years of working with industrial clients, the authors present many cutting-edge topics in a single, easily accessible source. Extensive case studies, including goals, data, and experimental designs, are also included, and the book's data sets can be found on a related FTP site, along with additional supplemental material. Chapter summaries provide a succinct outline of discussed methods, and extensive appendices direct readers to resources for further study. Experiments: Planning, Analysis, and Optimization, Third Edition is an excellent book for design of experiments courses at the upper-undergraduate and graduate levels. It is also a valuable resource for practicing engineers and statisticians.

Digital Image Processing with C++ presents the theory of digital image processing, and implementations of algorithms using a dedicated library. Processing a digital image means transforming its content (denoising, stylizing, etc.), or extracting information to solve a given problem (object recognition, measurement, motion estimation, etc.). This book presents the mathematical theories underlying digital image processing, as well as their practical implementation through examples of algorithms implemented in the C++ language, using the free and easy-to-use CImg library. Chapters cover in a broad way the field of digital image processing and proposes practical and functional implementations of each method theoretically described. The main topics covered include filtering in spatial and frequency domains, mathematical morphology, feature extraction and applications to segmentation, motion estimation, multispectral image processing and 3D visualization. Students or developers wishing to discover or specialize in this discipline, teachers and researchers wishing to quickly prototype new algorithms, or develop courses, will all find in this book material to discover image processing or deepen their knowledge in this field.

Exam Board: MEI Level: A-level Subject: Mathematics First Teaching: September 2017 First Exam: June 2018 An OCR endorsed textbook Help students to develop their knowledge and apply their reasoning to mathematical problems with textbooks that draw on the well-known MEI (Mathematics in Education and Industry) series, updated and tailored to the 2017 OCR (MEI) specification and developed by subject experts and MEI. - Ensure targeted development of reasoning and problem-solving skills with plenty of practice questions and structured exercises that build mathematical skills and techniques. - Build connections between topics, using real-world contexts to help develop mathematical modelling skills, thus providing a fuller and more coherent understanding of mathematical concepts. - Help students to overcome misconceptions and develop insight into problem solving with annotated worked examples. - Develop understanding and measure progress with graduated exercises that support students at every stage of their learning. - Provide clear paths of progression that combine pure and applied maths into a coherent whole.

How could Finance benefit from AI? How can AI techniques provide an edge? Moving well beyond simply speeding up computation, this book tackles AI for Finance from a range of perspectives including business, technology, research, and students. Covering aspects like algorithms, big data, and machine learning, this book answers these and many other questions.

Modern ecological and environmental sciences are dominated by observational data. As a result, traditional statistical training often leaves scientists ill-prepared for the data analysis tasks they encounter in their work. Bayesian methods provide a more robust and flexible tool for data analysis, as they enable information from different sources to be brought into the modelling process. Bayesian Applications in Evnironmental and Ecological Studies with R and Stan provides a Bayesian framework for model formulation, parameter estimation, and model evaluation in the context of analyzing environmental and ecological data. Features: An accessible overview of Bayesian methods in environmental and ecological studies Emphasizes the hypothetical deductive process, particularly model formulation Necessary background material on Bayesian inference and Monte Carlo simulation Detailed case studies, covering water quality monitoring and assessment, ecosystem response to urbanization, fisheries ecology, and more Advanced chapter on Bayesian applications, including Bayesian networks and a change point model Complete code for all examples, along with the data used in the book, are available via GitHub The book is primarily aimed at graduate students and researchers in the environmental and ecological sciences, as well as environmental management professionals. This is a group of people representing diverse subject matter fields, who could benefit from the potential power and flexibility of Bayesian methods.

This book introduces readers to spatiotemporal analytics that are extended from spatial statistics. Spatiotemporal analytics help analysts to quantitatively recognize and evaluate the spatial patterns and their temporal trends of a set of geographic events or objects. Spatiotemporal analyses are very important in geography, environmental sciences, economy, and many other domains. This book explains with very simple terms the concepts of spatiotemporal data and statistics, theories, and methods used. Each chapter introduces a case study as an example application for an in-depth learning process. The software used and the codes provided enable readers to not only learn statistics but also to use them effectively in their projects.

Focusing on the exploration of data with visual methods, this book presents methods and R code for producing high-quality static graphics, interactive visualizations, and animations of time series, spatial, and space-time data. Practical examples using real-world datasets help you understand how to apply the methods and code. Each of the three parts of the book is devoted to different types of data. In each part, the chapters are grouped according to the various visualization methods or data characteristics. Recent developments in the "htmlwidgets" family of packages are covered in this second edition with many new interactive graphics.

"Vaclav Smil is my favorite author... Numbers Don't Lie takes everything that makes his writing great and boils it down into an easy-to-read format. I unabashedly recommend this book to anyone who loves learning."--Bill Gates, GatesNotes From the author of How the World Really Works, an essential guide to understanding how numbers reveal the true state of our world--exploring a wide range of topics including energy, the environment, technology, transportation, and food production. Vaclav Smil's mission is to make facts matter. An environmental scientist, policy analyst, and a hugely prolific author, he is Bill Gates' go-to guy for making sense of our world. In Numbers Don't Lie, Smil answers questions such as: What's worse for the environment--your car or your phone? How much do the world's cows weigh (and what does it matter)? And what makes people happy? From data about our societies and populations, through measures of the fuels and foods that energize them, to the impact of transportation and inventions of our modern world--and how all of this affects the planet itself--in Numbers Don't Lie, Vaclav Smil takes us on a fact-finding adventure, using surprising statistics and illuminating graphs to challenge conventional thinking. Packed with fascinating information and memorable examples, Numbers Don't Lie reveals how the US is leading a rising worldwide trend in chicken consumption, that vaccination yields the best return on investment, and why electric cars aren't as great as we think (yet). Urgent and essential, with a mix of science, history, and wit--all in bite-sized chapters on a broad range of topics--Numbers Don't Lie inspires readers to interrogate what they take to be true.

Features Includes multiple case studies. Suitable for scientists and professionals working on methods for mosquito control. Provides a much-needed focal point for interdisciplinary discussion.

Designed to help you overcome your apprehension about statistics, UNDERSTANDING BASIC STATISTICS, ENHANCED, 7E, INTERNATIONAL METRIC EDITION provides plenty of guidance and informal advice as it demonstrates the links between statistics and the real world. The text's reader-friendly approach helps you grasp the concepts and see how they relate to your life. A complete technology package, including JMP (R) statistical software, gives you the tools you need to practice what you're learning and succeed in the course.