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This exciting new volume takes a positive spin on the field of statistics. Statistics is seen by students as difficult and boring, however, the authors of this book have eliminated that theory. Teaching Statistics: A Bag Of tricks, brings together a complete set of examples, demonstrations and projects that not only will increase class participation but will help to eliminate any negative feelings toward the area of statistics.

Didactics of mathematics --- Mathematical statistics --- Statistics --- 519.2 --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Study and teaching --- #SBIB:303H510 --- #SBIB:303H520 --- Methoden sociale wetenschappen: statistische technieken, algemeen --- Methoden sociale wetenschappen: techniek van de analyse, algemeen --- Study and teaching. --- Statistique --- Etude et enseignement --- Statistics - Study and teaching - Handbooks, manuals, etc.

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Communication is a critical yet often overlooked part of data science. Communicating with Data aims to help students and researchers write about their insights in a way that is both compelling and faithful to the data. General advice on science writing is also provided, including how to distill findings into a story and organize and revise the story, and how to write clearly, concisely, and precisely. This is an excellent resource for students who want to learn how to write about scientific findings, and for instructors who are teaching a science course in communication or a course with a writing component.Communicating with Data consists of five parts. Part I helps the novice learn to write by reading the work of others. Part II delves into the specifics of how to describe data at a level appropriate for publication, create informative and effective visualizations, and communicate an analysis pipeline through well-written, reproducible code. Part III demonstrates how to reduce a data analysis to a compelling story and organize and write the first draft of a technical paper. Part IV addresses revision; this includes advice on writing about statistical findings in a clear and accurate way, general writing advice, and strategies for proof reading and revising. Part V offers advice about communication strategies beyond the page, which include giving talks, building a professional network, and participating in online communities. This book also provides 22 portfolio prompts that extend the guidance and examples in the earlier parts of the book and help writers build their portfolio of data communication.

Communication in science. --- Technical writing. --- Science --- Data mining. --- Information scientifique --- Rédaction scientifique et technique --- Exploration de données. --- Data processing. --- Exploration de données --- Rédaction scientifique et technique --- Exploration de données

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This book uses a model we have developed for teaching mathematical statistics through in depth case studies. Traditional statistics texts have many small numer ical examples in each chapter to illustrate a topic in statistical theory. Here, we instead make a case study the centerpiece of each chapter. The case studies, which we call labs, raise interesting scienti?c questions, and ?guring out how to answer a question is the starting point for developing statistical theory. The labs are substan tial exercises; they have nontrivial solutions that leave room for different analyses of the data. In addition to providing the framework and motivation for studying topics in mathematical statistics, the labs help students develop statistical thinking. We feel that this approach integrates theoretical and applied statistics in a way not commonly encountered in an undergraduate text. The Student The book is intended for a course in mathematical statistics for juniors and seniors. We assume that students have had one year of calculus, including Taylor series, and a course in probability. We do not assume students have experience with statistical software so we incorporate lessons into our course on how to use the software.

Mathematical statistics. --- Mathematical statistics --- 519.2 --- regressie-analyse --- wiskundige statistiek --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Statistical methods --- Statistique mathématique --- EPUB-LIV-FT SPRINGER-B --- Statistics. --- Statistical Theory and Methods. --- Statistics . --- Statistical analysis --- Statistical data --- Statistical science --- Econometrics

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Web technologies are increasingly relevant to scientists working with data, for both accessing data and creating rich dynamic and interactive displays.  The XML and JSON data formats are widely used in Web services, regular Web pages and JavaScript code, and visualization formats such as SVG and KML for Google Earth and Google Maps.  In addition, scientists use HTTP and other network protocols to scrape data from Web pages, access REST and SOAP Web Services, and interact with NoSQL databases and text search applications.  This book provides a practical hands-on introduction to these technologies, including high-level functions the authors have developed for data scientists.  It describes strategies and approaches for extracting data from HTML, XML, and JSON formats and how to programmatically access data from the Web.  Along with these general skills, the authors illustrate several applications that are relevant to data scientists, such as reading and writing spreadsheet documents both locally and via GoogleDocs, creating interactive and dynamic visualizations, displaying spatial-temporal displays with Google Earth, and generating code from descriptions of data structures to read and write data.  These topics demonstrate the rich possibilities and opportunities to do new things with these modern technologies.  The book contains many examples and case-studies that readers can use directly and adapt to their own work.  The authors have focused on the integration of these technologies with the R statistical computing environment.  However, the ideas and skills presented here are more general, and statisticians who use other computing environments will also find them relevant to their work. Deborah Nolan is Professor of Statistics at University of California, Berkeley. Duncan Temple Lang is Associate Professor of Statistics at University of California, Davis and has been a member of both the S and R development teams.

Statistical science --- Mathematical statistics --- Computer science --- Programming --- Computer. Automation --- Javascript (informatica) --- computers --- informatica --- statistiek --- programmeertalen --- HTML (hypertext markup language) --- computerkunde

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This volume is our tribute to David A. Freedman, whom we regard as one of the great statisticians of our time.

Mathematical statistics. --- Probabilities. --- Freedman, David,