There are equally many advanced textbooks which delve into the far reaches of statistical theory, while bypassing practical applications. But between these two approaches is an unfilled gap, in which theory and practice merge at an intermediate level. Professor M. G. Bulmer's Principles of Statistics, originally published in 1965, was created to fill that need. The new, corrected Dover edition of Principles of Statistics makes this invaluable mid-level text available once again for the classroom or for self-study.Principles of Statistics was created primarily for the student of natural sciences, the social scientist, the undergraduate mathematics student, or anyone familiar with the basics of mathematical language. It assumes no previous knowledge of statistics or probability; nor is extensive mathematical knowledge necessary beyond a familiarity with the fundamentals of differential and integral calculus. (The calculus is used primarily for ease of notation; skill in the techniques of integration is not necessary in order to understand the text.)Professor Bulmer devotes the first chapters to a concise, admirably clear description of basic terminology and fundamental statistical theory: abstract concepts of probability and their applications in dice games, Mendelian heredity, etc.; definitions and examples of discrete and continuous random variables; multivariate distributions and the descriptive tools used to delineate them; expected values; etc. The book then moves quickly to more advanced levels, as Professor Bulmer describes important distributions (binomial, Poisson, exponential, normal, etc.), tests of significance, statistical inference, point estimation, regression, and correlation. Dozens of exercises and problems appear at the end of various chapters, with answers provided at the back of the book. Also included are a number of statistical tables and selected references.

    Each recipe tackles a specific problem with a solution you can apply to your own project, and includes a discussion of how and why the recipe works.Most of the recipes use the ggplot2 package, a powerful and flexible way to make graphs in R. If you have a basic understanding of the R language, you're ready to get started.Use R's default graphics for quick exploration of dataCreate a variety of bar graphs, line graphs, and scatter plotsSummarize data distributions with histograms, density curves, box plots, and other examplesProvide annotations to help viewers interpret dataControl the overall appearance of graphicsRender data groups alongside each other for easy comparisonUse colors in plotsCreate network graphs, heat maps, and 3D scatter plotsStructure data for graphing

    Its big-picture, calculus-based approach makes it an especially authoriatative reference for engineering and science majors. Now thoroughly update, this second edition includes vital new coverage of order statistics, best critical regions, likelihood ratio tests, and other key topics.

    But trust me. Salsa dancing is a practice as well as a metaphor for a kind of research that will make your life easier and better.""Savvy, witty, and sensible, this unique book is both a handbook for defining and completing a research project, and an astute introduction to the neglected history and changeable philosophy of modern social science. In this volume, Kristin Luker guides novice researchers in: knowing the difference between an area of interest and a research topicdefining the relevant parts of a potentially infinite research literaturemastering sampling, operationalization, and generalizationunderstanding which research methods best answer your questionsbeating writer's blockMost important, she shows how friendships, nonacademic interests, and even salsa dancing can make for a better researcher.""You know about setting the kitchen timer and writing for only an hour, or only 15 minutes if you are feeling particularly anxious. I wrote a fairly large part of this book feeling exactly like that. If I can write an entire book 15 minutes at a time, so can you.""

    A lady never eats a meal while behind the wheel of a car. A lady makes it a point to know the names of the teams playing in the Super Bowl. The roles women play in society today are drastically different than they were 25 years ago, and some of the rules are different as well. Patterned after the successful How to Be a Gentleman, How to Be a Lady is a practical book of etiquette that is not snobbish, boring or intimidating. It is designed for a new century by discussing such issues as how to dress for the workplace, the importance of knowing self defense, and how to tactfully ask for the sexual history of a person with whom a lady is considering an intimate relationship.

    You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples--ready for immediate application to your next graph or presentation.Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to:Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data--Storytelling with Data will give you the skills and power to tell it!

    In response, UNESCO launched an international safeguarding campaign, which ultimately led to the list of World Heritage sites. In clear text that highlights all the fascinating facts, World Heritage Sites details all 878 properties, including the 20 American and 15 Canadian sites. Covering 141 countries, the World Heritage List has proved to be a valuable tool in the battle to preserve much of the world's cultural and natural heritage. Its strict criteria result in only the world's most spectacular and extraordinary sites making it onto the list, including: The Acropolis in Athens, The Statue of Liberty in New York, The Rideau Canal in eastern Ontario, Waterton Glacier International Peace Park, the world's first international peace park, which straddles the US-Canada border, The ancient Nabataean city of Petra in Jordan, The remarkable Dazu Rock Carvings in China, Cahokia Mounds State Historic Site in Missouri, the largest pre- Columbian settlement north of Mexico, The unique ecosystem of the Serengeti in Tanzania, And 870 more. Featuring gorgeous photographs and a location map for every site, World Heritage Sites is uniquely comprehensive. The United Nations Educational, Scientific and Cultural Organization (UNESCO) was founded in 1945 with the ambitious goal to build peace in the minds of men and women through education, social and natural science, culture and communication.

    This comprehensive and beautiful guide won the World Cookbook prize as the best book on an Asian subject when first published in hardback in 2001.

    A finished book—or even steady journal articles—may seem like an impossible dream. But, as Joli Jensen proves, it really is possible to write happily and productively in academe. Jensen begins by busting the myth that universities are supportive writing environments.  She points out that academia, an arena dedicated to scholarship, offers pressures that actually prevent scholarly writing. She shows how to acknowledge these less-than-ideal conditions, and how to keep these circumstances from draining writing time and energy. Jensen introduces tools and techniques that encourage frequent, low-stress writing. She points out common ways writers stall and offers workarounds that maintain productivity. Her focus is not on content, but on how to overcome whatever stands in the way of academic writing.Write No Matter What draws on popular and scholarly insights into the writing process and stems from Jensen’s experience designing and directing a faculty writing program. With more than three decades as an academic writer, Jensen knows what really helps and hinders the scholarly writing process for scholars in the humanities, social sciences,and sciences. Cut down the academic sword of Damocles, Jensen advises. Learn how to write often and effectively, without pressure or shame. With her encouragement, writers of all levels will find ways to create the writing support they need and deserve.

    Morgenstern teaches innovative "grab and go" solutions that can be processed in as few as fifteen minutes to dramatically improve performance and efficiency, including: -Beware of multitasking. Scattering your efforts makes for a longer day. -Dance near the revenue line. Making or saving money is where your greatest value lies. -Crunch your container. Shorten your workday by thirty minutes and you will get more done. -Trust your truth. Never undervalue your unique self, skills, and point of view. If you've ever wondered, Is it me or is it them? Julie Morgenstern's practical "inside out" approach will help you diagnose the source of each workplace problem and solve it quickly.

    With the sophisticated algorithms in this book, you can write smart programs to access interesting datasets from other web sites, collect data from users of your own applications, and analyze and understand the data once you've found it.Programming Collective Intelligence takes you into the world of machine learning and statistics, and explains how to draw conclusions about user experience, marketing, personal tastes, and human behavior in general -- all from information that you and others collect every day. Each algorithm is described clearly and concisely with code that can immediately be used on your web site, blog, Wiki, or specialized application. This book explains:Collaborative filtering techniques that enable online retailers to recommend products or media Methods of clustering to detect groups of similar items in a large dataset Search engine features -- crawlers, indexers, query engines, and the PageRank algorithm Optimization algorithms that search millions of possible solutions to a problem and choose the best one Bayesian filtering, used in spam filters for classifying documents based on word types and other features Using decision trees not only to make predictions, but to model the way decisions are made Predicting numerical values rather than classifications to build price models Support vector machines to match people in online dating sites Non-negative matrix factorization to find the independent features in a dataset Evolving intelligence for problem solving -- how a computer develops its skill by improving its own code the more it plays a game Each chapter includes exercises for extending the algorithms to make them more powerful. Go beyond simple database-backed applications and put the wealth of Internet data to work for you. "Bravo! I cannot think of a better way for a developer to first learn these algorithms and methods, nor can I think of a better way for me (an old AI dog) to reinvigorate my knowledge of the details."-- Dan Russell, Google "Toby's book does a great job of breaking down the complex subject matter of machine-learning algorithms into practical, easy-to-understand examples that can be directly applied to analysis of social interaction across the Web today. If I had this book two years ago, it would have saved precious time going down some fruitless paths."-- Tim Wolters, CTO, Collective Intellect

    The applications and computations in operations research are emphasized. Significantly revised, this text streamlines the coverage of the theory, applications, and computations of operations research. Numerical examples are effectively used to explain complex mathematical concepts. A separate chapter of fully analyzed applications aptly demonstrates the diverse use of OR. The popular commercial and tutorial software AMPL, Excel, Excel Solver, and Tora are used throughout the book to solve practical problems and to test theoretical concepts. New materials include Markov chains, TSP heuristics, new LP models, and a totally new simplex-based approach to LP sensitivity analysis.

    It is designed as a reference for students in the physical sciences and engineering.

    Because the computer gathers knowledge from experience, there is no need for a human computer operator to formally specify all the knowledge that the computer needs. The hierarchy of concepts allows the computer to learn complicated concepts by building them out of simpler ones; a graph of these hierarchies would be many layers deep. This book introduces a broad range of topics in deep learning.The text offers mathematical and conceptual background, covering relevant concepts in linear algebra, probability theory and information theory, numerical computation, and machine learning. It describes deep learning techniques used by practitioners in industry, including deep feedforward networks, regularization, optimization algorithms, convolutional networks, sequence modeling, and practical methodology; and it surveys such applications as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames. Finally, the book offers research perspectives, covering such theoretical topics as linear factor models, autoencoders, representation learning, structured probabilistic models, Monte Carlo methods, the partition function, approximate inference, and deep generative models.Deep Learning can be used by undergraduate or graduate students planning careers in either industry or research, and by software engineers who want to begin using deep learning in their products or platforms. A website offers supplementary material for both readers and instructors.

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics