This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    Individual chapters discuss the representativeness and availability heuristics, problems in judging covariation and control, overconfidence, multistage inference, social perception, medical diagnosis, risk perception, and methods for correcting and improving judgments under uncertainty. About half of the chapters are edited versions of classic articles; the remaining chapters are newly written for this book. Most review multiple studies or entire subareas of research and application rather than describing single experimental studies. This book will be useful to a wide range of students and researchers, as well as to decision makers seeking to gain insight into their judgments and to improve them.

    Wouldn't it be wonderful if we could actually visualize data in such a way that we could maximize its potential and tell a story in a clear, concise manner? Thanks to the creative genius of Nathan Yau, we can. With this full-color book, data visualization guru and author Nathan Yau uses step-by-step tutorials to show you how to visualize and tell stories with data. He explains how to gather, parse, and format data and then design high quality graphics that help you explore and present patterns, outliers, and relationships.Presents a unique approach to visualizing and telling stories with data, from a data visualization expert and the creator of flowingdata.com, Nathan Yau Offers step-by-step tutorials and practical design tips for creating statistical graphics, geographical maps, and information design to find meaning in the numbers Details tools that can be used to visualize data-native graphics for the Web, such as ActionScript, Flash libraries, PHP, and JavaScript and tools to design graphics for print, such as R and Illustrator Contains numerous examples and descriptions of patterns and outliers and explains how to show them Visualize This demonstrates how to explain data visually so that you can present your information in a way that is easy to understand and appealing.

    Ian Hacking here presents a philosophical critique of early ideas about probability, induction and statistical inference and the growth of this new family of ideas in the fifteenth, sixteenth and seventeenth centuries. The contemporary debate centres round such figures as Pascal, Leibniz and Jacques Bernoulli. What brought about the change in ideas? The author invokes in his explanation a wider intellectual framework involving the growth of science, economics and the theology of the period.

    However, these activities can be viewed as two facets of the same field, and together they have undergone substantial development over the past ten years. In particular, Bayesian methods have grown from a specialist niche to become mainstream, while graphical models have emerged as a general framework for describing and applying probabilistic models. Also, the practical applicability of Bayesian methods has been greatly enhanced through the development of a range of approximate inference algorithms such as variational Bayes and expectation propagation. Similarly, new models based on kernels have had a significant impact on both algorithms and applications. This new textbook reflects these recent developments while providing a comprehensive introduction to the fields of pattern recognition and machine learning. It is aimed at advanced undergraduates or first-year PhD students, as well as researchers and practitioners, and assumes no previous knowledge of pattern recognition or machine learning concepts. Knowledge of multivariate calculus and basic linear algebra is required, and some familiarity with probabilities would be helpful though not essential as the book includes a self-contained introduction to basic probability theory.

    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 theoryData 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.

    This text provides a simple and straightforward introduction to econometrics for the beginner. The book is designed to help students understand econometric techniques through extensive examples, careful explanations, and a wide variety of problem material. In each of the editions, I have tried to incorporate major developments in the field in an intuitive and informative way without resort to matrix algebra, calculus, or statistics beyond the introductory level. The fourth edition continues that tradition.

    Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications.Reinforcement learning, one of the most active research areas in artificial intelligence, is a computational approach to learning whereby an agent tries to maximize the total amount of reward it receives when interacting with a complex, uncertain environment. In Reinforcement Learning, Richard Sutton and Andrew Barto provide a clear and simple account of the key ideas and algorithms of reinforcement learning. Their discussion ranges from the history of the field's intellectual foundations to the most recent developments and applications. The only necessary mathematical background is familiarity with elementary concepts of probability.The book is divided into three parts. Part I defines the reinforcement learning problem in terms of Markov decision processes. Part II provides basic solution methods: dynamic programming, Monte Carlo methods, and temporal-difference learning. Part III presents a unified view of the solution methods and incorporates artificial neural networks, eligibility traces, and planning; the two final chapters present case studies and consider the future of reinforcement learning.

    This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models. In addition, he presents basic tools for analyzing dynamic systems (including linear representations, autocovariance generating functions, spectral analysis, and the Kalman filter) in a way that integrates economic theory with the practical difficulties of analyzing and interpreting real-world data. Time Series Analysis fills an important need for a textbook that integrates economic theory, econometrics, and new results.The book is intended to provide students and researchers with a self-contained survey of time series analysis. It starts from first principles and should be readily accessible to any beginning graduate student, while it is also intended to serve as a reference book for researchers.-- "Journal of Economics"

    In this indispensable volume, a primer for the information age, Hans Christian von Baeyer presents a clear description of what information is, how concepts of its measurement, meaning, and transmission evolved, and what its ever-expanding presence portends for the future. Information is poised to replace matter as the primary stuff of the universe, von Baeyer suggests; it will provide a new basic framework for describing and predicting reality in the twenty-first century. Despite its revolutionary premise, von Baeyer's book is written simply in a straightforward fashion, offering a wonderfully accessible introduction to classical and quantum information. Enlivened with anecdotes from the lives of philosophers, mathematicians, and scientists who have contributed significantly to the field, Information conducts readers from questions of subjectivity inherent in classical information to the blurring of distinctions between computers and what they measure or store in our quantum age. A great advance in our efforts to define and describe the nature of information, the book also marks an important step forward in our ability to exploit information--and, ultimately, to transform the nature of our relationship with the physical universe. (20040301)

    Authors Drew Conway and John Myles White help you understand machine learning and statistics tools through a series of hands-on case studies, instead of a traditional math-heavy presentation.Each chapter focuses on a specific problem in machine learning, such as classification, prediction, optimization, and recommendation. Using the R programming language, you'll learn how to analyze sample datasets and write simple machine learning algorithms. "Machine Learning for Hackers" is ideal for programmers from any background, including business, government, and academic research.Develop a naive Bayesian classifier to determine if an email is spam, based only on its textUse linear regression to predict the number of page views for the top 1,000 websitesLearn optimization techniques by attempting to break a simple letter cipherCompare and contrast U.S. Senators statistically, based on their voting recordsBuild a "whom to follow" recommendation system from Twitter data

    and trying, and trying some more? How far do you go to achieve your dream of having children?So Close is the heart wrenching, exhilarating, devastatingly funny story of Tertia Albertyn's battle with infertility. Tertia wanted a baby so badly she went through nine IVFs. Most people give up after the third.I don't think I am being brave at all. I am just too terrified NOT to try again.In her worst nightmare she could never have imagined that making a baby would take her four years, each treatment bringing her and her husband Marko closer and closer to creating their family.During Tertia's journey everything that can go wrong does go wrong. Until, finally, everything goes just right.Tertia is as hilarious as she is irrepressible, as approachable as she is knowledgeable. If you are struggling with infertility, have triumphed over infertility or have felt empathy with someone who is going through this experience, you will find a friend in Tertia.

    Soon she has divorced her husband and, in the throes of newfound passion, finds herself quickly remarried to this extraordinary man.Her new husband has unlocked something in Marian and she feels high on life as never before, even when a darker side to his nature begins to reveal itself.Nothing can prepare her for what is to follow, though, as devastating circumstances lead her on a trail to Daniel's home in Ireland, and in turn back to America, as she tries to come to terms with the enigmatic nature of the man for whom she threw it all away - only to discover that nothing was what it seemed. The Husband is a gripping human story of love in its many guises, of losing everything and ultimately - in the small Midlands village of Glanmilish - of rediscovering the meaning of family.

    Ideal for introducing the novice researcher to the theory and practice of qualitative research, this text opens students to the diverse possibilities within this inquiry approach, while helping them understand how to design and implement specific research methods. The author's accessible writing style, the wealth of examples, and the numerous exercises provide opportunities for practicing and refining the skills of becoming a qualitative researcher. The new edition focuses on the development of research proposals (Ch. 2); the history and concerns of institutional review boards (IRBs) and issues qualitative researchers sometimes confront when submitting proposals (Ch. 6); greater information and examples on coding and thematic analysis, while also introducing other approaches to data analysis (Ch. 7); and arts based research through a chapter that encourages consideration of creative ways to approach and represent inquiry (Ch. 9). Chapter 10 looks at sharing research results through participation at conferences and in publications.

    This book provides an applications-oriented introduction to multivariate data analysis for the non-statistician, by focusing on the fundamental concepts that affect the use of specific techniques.