R has traditionally been difficult for non-statisticians to learn, and most R books assume far too much knowledge to be of help. R for Everyone is the solution. Drawing on his unsurpassed experience teaching new users, professional data scientist Jared P. Lander has written the perfect tutorial for anyone new to statistical programming and modeling. Organized to make learning easy and intuitive, this guide focuses on the 20 percent of R functionality you'll need to accomplish 80 percent of modern data tasks. Lander's self-contained chapters start with the absolute basics, offering extensive hands-on practice and sample code. You'll download and install R; navigate and use the R environment; master basic program control, data import, and manipulation; and walk through several essential tests. Then, building on this foundation, you'll construct several complete models, both linear and nonlinear, and use some data mining techniques. By the time you're done, you won't just know how to write R programs, you'll be ready to tackle the statistical problems you care about most. COVERAGE INCLUDES - Exploring R, RStudio, and R packages - Using R for math: variable types, vectors, calling functions, and more - Exploiting data structures, including data.frames, matrices, and lists - Creating attractive, intuitive statistical graphics - Writing user-defined functions - Controlling program flow with if, ifelse, and complex checks - Improving program efficiency with group manipulations - Combining and reshaping multiple datasets - Manipulating strings using R's facilities and regular expressions - Creating normal, binomial, and Poisson probability distributions - Programming basic statistics: mean, standard deviation, and t-tests - Building linear, generalized linear, and nonlinear models - Assessing the quality of models and variable selection - Preventing overfitting, using the Elastic Net and Bayesian methods - Analyzing univariate and multivariate time series data - Grouping data via K-means and hierarchical clustering - Preparing reports, slideshows, and web pages with knitr - Building reusable R packages with devtools and Rcpp - Getting involved with the R global community

    Theoretical, yet applied. Statistics for Business and Economics, Eleventh Edition, gives you the best of both worlds. Using a rich array of applications from a variety of industries, McClave/Sincich/Benson clearly demonstrates how to use statistics effectively in a business environment.The book focuses on developing statistical thinking so the reader can better assess the credibility and value of inferences made from data. As consumers and future producers of statistical inferences, readers are introduced to a wide variety of data collection and analysis techniques to help them evaluate data and make informed business decisions. As with previous editions, this revision offers an abundance of applications with many new and updated exercises that draw on real business situations and recent economic events. The authors assume a background of basic algebra.

    Each technique is introduced with a simple, jargon-free explanation, practical examples, and hands-on guidance for solving real problems with Excel or a TI-83/84 series calculator, including Plus models. Hate math? No sweat. You'll be amazed how little you need! For those who do have an interest in mathematics, optional "Equation Blackboard" sections review the equations that provide the foundations for important concepts. David M. Levine is a much-honored innovator in statistics education. He is Professor Emeritus of Statistics and Computer Information Systems at Bernard M. Baruch College (CUNY), and co-author of several best-selling books, including Statistics for Managers using Microsoft Excel, Basic Business Statistics, Quality Management, and Six Sigma for Green Belts and Champions. Instructional designer David F. Stephan pioneered the classroom use of personal computers, and is a leader in making Excel more accessible to statistics students. He has co-authored several textbooks with David M. Levine. Here's just some of what you'll learn how to do... Use statistics in your everyday work or study Perform common statistical tasks using a Texas Instruments statistical calculator or Microsoft Excel Build and interpret statistical charts and tables "Test Yourself" at the end of each chapter to review the concepts and methods that you learned in the chapter Work with mean, median, mode, standard deviation, Z scores, skewness, and other descriptive statistics Use probability and probability distributions Work with sampling distributions and confidence intervals Test hypotheses and decision-making risks with Z, t, Chi-Square, ANOVA, and other techniques Perform regression analysis and modeling The easy, practical introduction to statistics--for everyone! Thought you couldn't learn statistics? Think again. You can--and you will!

      Statistics are everywhere, as integral to science as they are to business, and in the popular media hundreds of times a day. In this age of big data, a basic grasp of statistical literacy is more important than ever if we want to separate the fact from the fiction, the ostentatious embellishments from the raw evidence -- and even more so if we hope to participate in the future, rather than being simple bystanders. In The Art of Statistics, world-renowned statistician David Spiegelhalter shows readers how to derive knowledge from raw data by focusing on the concepts and connections behind the math. Drawing on real world examples to introduce complex issues, he shows us how statistics can help us determine the luckiest passenger on the Titanic, whether a notorious serial killer could have been caught earlier, and if screening for ovarian cancer is beneficial. The Art of Statistics not only shows us how mathematicians have used statistical science to solve these problems -- it teaches us how we too can think like statisticians. We learn how to clarify our questions, assumptions, and expectations when approaching a problem, and -- perhaps even more importantly -- we learn how to responsibly interpret the answers we receive. Combining the incomparable insight of an expert with the playful enthusiasm of an aficionado, The Art of Statistics is the definitive guide to stats that every modern person needs.

    The book assumes no prior knowledge, taking the student through every stage of their research project in manageable steps. Advice on planning and conducting studies, analyzing data, and writing up practical reports is given, and examples are provided, as well as advice on how to report results in conventional (APA) style. Unlike other introductory texts, there is practical guidance on qualitative research, as well as discussion of issues of bias, interpretation, and variance. Content on qualitative methods has been expanded for the fifth edition and now includes additional material on widely used methods, such as grounded theory, thematic analysis, interpretive phenomenological analysis (IPA), and discourse analysis. The book provides clear coverage of statistical procedures, and includes everything needed at an undergraduate level from nominal level tests, to multi-factorial ANOVA designs, multiple regression, and log linear analysis. In addition, the book provides detailed and illustrated SPSS textbook. Each chapter contains a self-test glossary, key terms, and exercises, ensuring that key concepts have been understood. Students are further supported. Students are further supported by an accompanying website that provides additional exercises, revision flash cards, links to further reading, and data for use with SPSS. The website will also include updated coverage of SPSS should a new version be launched. The bestselling research methods text for over a decade, Research Methods and Statistics in Psychology remains an invaluable resource for students of psychology throughout their studies.

    The Fifth Edition features updated information throughout, including new theories and models such as the Healthy Action Process Approach (HAPA) and the Community Readiness Model (CRM), sections on grant writing and preparing a budget, real-life examples of marketing principles and processes, and a new classification system for evaluation approaches and designs. Health Education, Health Promotion, Health Educators, and Program Planning, Models for Program Planning in Health Promotion, Starting the Planning Process, Assessing Needs, Measurement, Measures, Measurement Instruments and Sampling, Mission Statement, Goals, and Objectives, Theories and Models Commonly Used for Health Promotion Interventions, Interventions, Community Organizing and Community Building, Identification and Allocation of Resources, Marketing: Making Sure Programs Respond to Wants and Needs of Consumers, Implementation: Strategies and Associated Concerns, Evaluation: An Overview, Evaluation Approaches and Designs, Data Analysis and Reporting. Intended for those interested in learning the basics of planning, implementing, and evaluating health promotion programs

    Incorporating biological, medicinal, and environmental applications, it builts an art program. Highlighting the art program are micro-to-macro art pieces that visually guide students to conceptually understand organic chemistry.

    It turns out that all the buzz is well earned. I think this is the best book currently available for learning Python. I would recommend Chun's book over Learning Python (O'Reilly), Programming Python (O'Reilly), or The Quick Python Book (Manning)." --David Mertz, Ph.D., IBM DeveloperWorks(R) "I have been doing a lot of research [on] Python for the past year and have seen a number of positive reviews of your book. The sentiment expressed confirms the opinion that Core Python Programming is now considered the standard introductory text." --Richard Ozaki, Lockheed Martin "Finally, a book good enough to be both a textbook and a reference on the Python language now exists." --Michael Baxter, Linux Journal "Very well written. It is the clearest, friendliest book I have come across yet for explaining Python, and putting it in a wider context. It does not presume a large amount of other experience. It does go into some important Python topics carefully and in depth. Unlike too many beginner books, it never condescends or tortures the reader with childish hide-and-seek prose games. [It] sticks to gaining a solid grasp of Python syntax and structure." --http: //python.org bookstore Web site "[If ] I could only own one Python book, it would be Core Python Programming by Wesley Chun. This book manages to cover more topics in more depth than Learning Python but includes it all in one book that also more than adequately covers the core language. [If] you are in the market for just one book about Python, I recommend this book. You will enjoy reading it, including its wry programmer's wit. More importantly, you will learn Python. Even more importantly, you will find it invaluable in helping you in your day-to-day Python programming life. Well done, Mr. Chun!" --Ron Stephens, Python Learning Foundation "I think the best language for beginners is Python, without a doubt. My favorite book is Core Python Programming." --s003apr, MP3Car.com Forums "Personally, I really like Python. It's simple to learn, completely intuitive, amazingly flexible, and pretty darned fast. Python has only just started to claim mindshare in the Windows world, but look for it to start gaining lots of support as people discover it. To learn Python, I'd start with Core Python Programming by Wesley Chun." --Bill Boswell, MCSE, Microsoft Certified Professional Magazine Online "If you learn well from books, I suggest Core Python Programming. It is by far the best I've found. I'm a Python newbie as well and in three months time I've been able to implement Python in projects at work (automating MSOffice, SQL DB stuff, etc.)." --ptonman, Dev Shed Forums "Python is simply a beautiful language. It's easy to learn, it's cross-platform, and it works. It has achieved many of the technical goals that Java strives for. A one-sentence description of Python would be: 'All other languages appear to have evolved over time--but Python was designed.' And it was designed well. Unfortunately, there aren't a large number of books for Python. The best one I've run across so far is Core Python Programming." --Chris Timmons, C. R. Timmons Consulting "If you like the Prentice Hall Core series, another good full-blown treatment to consider would be Core Python Programming. It addresses in elaborate concrete detail many practical topics that get little, if any, coverage in other books." --Mitchell L Model, MLM Consulting "Core Python Programming is an amazingly easy read! The liberal use of examples helps clarify some of the more subtle points of the language. And the comparisons to languages with which I'm already familiar (C/C++/Java) get you programming in record speed." --Michael Santos, Ph.D., Green Hills Software The Complete Developer's Guide to Python New to Python? The definitive guide to Python development for experienced programmersCovers core language features thoroughly, including those found in the latest Python releases--learn more than just the syntax!Learn advanced topics such as regular expressions, networking, multithreading, GUI, Web/CGI, and Python extensionsIncludes brand-new material on databases, Internet clients, Java/Jython, and Microsoft Office, plus Python 2.6 and 3Presents hundreds of code snippets, interactive examples, and practical exercises to strengthen your Python skills Python is an agile, robust, expressive, fully object-oriented, extensible, and scalable programming language. It combines the power of compiled languages with the simplicity and rapid development of scripting languages. In Core Python Programming, Second Edition , leading Python developer and trainer Wesley Chun helps you learn Python quickly and comprehensively so that you can immediately succeed with any Python project. Using practical code examples, Chun introduces all the fundamentals of Python programming: syntax, objects and memory management, data types, operators, files and I/O, functions, generators, error handling and exceptions, loops, iterators, functional programming, object-oriented programming and more. After you learn the core fundamentals of Python, he shows you what you can do with your new skills, delving into advanced topics, such as regular expressions, networking programming with sockets, multithreading, GUI development, Web/CGI programming and extending Python in C. This edition reflects major enhancements in the Python 2.x series, including 2.6 and tips for migrating to 3. It contains new chapters on database and Internet client programming, plus coverage of many new topics, including new-style classes, Java and Jython, Microsoft Office (Win32 COM Client) programming, and much more. Learn professional Python style, best practices, and good programming habitsGain a deep understanding of Python's objects and memory model as well as its OOP features, including those found in Python's new-style classesBuild more effective Web, CGI, Internet, and network and other client/server applicationsLearn how to develop your own GUI applications using Tkinter and other toolkits available for PythonImprove the performance of your Python applications by writing extensions in C and other languages, or enhance I/O-bound applications by using multithreadingLearn about Python's database API and how to use a variety of database systems with Python, including MySQL, Postgres, and SQLiteFeatures appendices on Python 2.6 & 3, including tips on migrating to the next generation! Core Python Programming delivers Systematic, expert coverage of Python's core featuresPowerful insights for developing complex applicationsEasy-to-use tables and charts detailing Python modules, operators, functions, and methodsDozens of professional-quality code examples, from quick snippets to full-fledged applications

    Bayesian statistics is taught in most first-year statistics classes across the nation, but there is one major problem that many students (and others who are interested in the theorem) face. The theorem is not intuitive for most people, and understanding how it works can be a challenge, especially because it is often taught without visual aids. In this guide, we unpack the various components of the theorem and provide a basic overview of how it works - and with illustrations to help. Three scenarios - the flu, breathalyzer tests, and peacekeeping - are used throughout the booklet to teach how problems involving Bayes Theorem can be approached and solved. Over 60 hand-drawn visuals are included throughout to help you work through each problem as you learn by example. The illustrations are simple, hand-drawn, and in black and white. For those interested, we have also included sections typically not found in other beginner guides to Bayes Rule. These include: A short tutorial on how to understand problem scenarios and find P(B), P(A), and P(B|A). For many people, knowing how to approach scenarios and break them apart can be daunting. In this booklet, we provide a quick step-by-step reference on how to confidently understand scenarios.A few examples of how to think like a Bayesian in everyday life. Bayes Rule might seem somewhat abstract, but it can be applied to many areas of life and help you make better decisions. It is a great tool that can help you with critical thinking, problem-solving, and dealing with the gray areas of life. A concise history of Bayes Rule. Bayes Theorem has a fascinating 200+ year history, and we have summed it up for you in this booklet. From its discovery in the 1700’s to its being used to break the German’s Enigma Code during World War 2, its tale is quite phenomenal.Fascinating real-life stories on how Bayes formula is used in everyday life.From search and rescue to spam filtering and driverless cars, Bayes is used in many areas of modern day life. We have summed up 3 examples for you and provided an example of how Bayes could be used.An expanded definitions, notations, and proof section.We have included an expanded definitions and notations sections at the end of the booklet. In this section we define core terms more concretely, and also cover additional terms you might be confused about. A recommended readings section.From The Theory That Would Not Die to a few other books, there are a number of recommendations we have for further reading. Take a look! If you are a visual learner and like to learn by example, this intuitive booklet might be a good fit for you. Bayesian statistics is an incredibly fascinating topic and likely touches your life every single day. It is a very important tool that is used in data analysis throughout a wide-range of industries - so take an easy dive into the theorem for yourself with a visual approach!If you are looking for a short beginners guide packed with visual examples, this booklet is for you.

    Readers are provided once again with an instructive mix of mathematics, physics, statistics, and information theory.All the essential topics in information theory are covered in detail, including entropy, data compression, channel capacity, rate distortion, network information theory, and hypothesis testing. The authors provide readers with a solid understanding of the underlying theory and applications. Problem sets and a telegraphic summary at the end of each chapter further assist readers. The historical notes that follow each chapter recap the main points.The Second Edition features: * Chapters reorganized to improve teaching * 200 new problems * New material on source coding, portfolio theory, and feedback capacity * Updated referencesNow current and enhanced, the Second Edition of Elements of Information Theory remains the ideal textbook for upper-level undergraduate and graduate courses in electrical engineering, statistics, and telecommunications.

    This contemporary and student-relevant text gives you all the key material you need for your introductory course, plus it will show you that anthropology is for you! With real world applications of the principles and practices of anthropology, this book will help you learn to appreciate other cultures as well as your own. Apply what you learn in this course to those situations that you are likely to encounter in your personal and professional life. What can you do with anthropology today? Check out the real-life examples of cross-cultural misunderstandings and issues (in our popular "Cross-Cultural Miscues" features) to view 'culture at work.' Also, the book takes a look at specialized vocabularies as illustrated by "chickspeak" (the language of single, urban, upwardly mobile women), the war in Iraq, environmental degradation, and other contemporary topics.

    Peter Flach's clear, example-based approach begins by discussing how a spam filter works, which gives an immediate introduction to machine learning in action, with a minimum of technical fuss. Flach provides case studies of increasing complexity and variety with well-chosen examples and illustrations throughout. He covers a wide range of logical, geometric and statistical models and state-of-the-art topics such as matrix factorisation and ROC analysis. Particular attention is paid to the central role played by features. The use of established terminology is balanced with the introduction of new and useful concepts, and summaries of relevant background material are provided with pointers for revision if necessary. These features ensure Machine Learning will set a new standard as an introductory textbook.

    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.

    Designed for two-semester, beginning graduate courses in Mathematical Statistics, and for senior undergraduate Mathematics, Statistics, and Actuarial Science majors, this text retains its ongoing features and continues to provide students with background material.

    Rather than a "how to" for hands-on techies, the book entices lay-readers and experts alike by covering new case studies and the latest state-of-the-art techniques.You have been predicted — by companies, governments, law enforcement, hospitals, and universities. Their computers say, "I knew you were going to do that!" These institutions are seizing upon the power to predict whether you're going to click, buy, lie, or die.Why? For good reason: predicting human behavior combats financial risk, fortifies healthcare, conquers spam, toughens crime fighting, and boosts sales.How? Prediction is powered by the world's most potent, booming unnatural resource: data. Accumulated in large part as the by-product of routine tasks, data is the unsalted, flavorless residue deposited en masse as organizations churn away. Surprise! This heap of refuse is a gold mine. Big data embodies an extraordinary wealth of experience from which to learn.Predictive analytics unleashes the power of data. With this technology, the computer literally learns from data how to predict the future behavior of individuals. Perfect prediction is not possible, but putting odds on the future — lifting a bit of the fog off our hazy view of tomorrow — means pay dirt.In this rich, entertaining primer, former Columbia University professor and Predictive Analytics World founder Eric Siegel reveals the power and perils of prediction: -What type of mortgage risk Chase Bank predicted before the recession. -Predicting which people will drop out of school, cancel a subscription, or get divorced before they are even aware of it themselves. -Why early retirement decreases life expectancy and vegetarians miss fewer flights. -Five reasons why organizations predict death, including one health insurance company. -How U.S. Bank, European wireless carrier Telenor, and Obama's 2012 campaign calculated the way to most strongly influence each individual. -How IBM's Watson computer used predictive modeling to answer questions and beat the human champs on TV's Jeopardy! -How companies ascertain untold, private truths — how Target figures out you're pregnant and Hewlett-Packard deduces you're about to quit your job. -How judges and parole boards rely on crime-predicting computers to decide who stays in prison and who goes free. -What's predicted by the BBC, Citibank, ConEd, Facebook, Ford, Google, IBM, the IRS, Match.com, MTV, Netflix, Pandora, PayPal, Pfizer, and Wikipedia. A truly omnipresent science, predictive analytics affects everyone, every day. Although largely unseen, it drives millions of decisions, determining whom to call, mail, investigate, incarcerate, set up on a date, or medicate.Predictive analytics transcends human perception. This book's final chapter answers the riddle: What often happens to you that cannot be witnessed, and that you can't even be sure has happened afterward — but that can be predicted in advance?Whether you are a consumer of it — or consumed by it — get a handle on the power of Predictive Analytics.