Teaching Statistics: A Bag of Tricks

This 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.

Business & Money

Gelman, Andrew

Nolan, Deborah

Education & Reference

Oxford University Press

This 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.

Science & Math

Mathematics

Students in the sciences, economics, social sciences, and medicine take an introductory statistics course. And yet statistics can be notoriously difficult for instructors to teach and for students to learn. To help overcome these challenges, Gelman and Nolan have put together this fascinating and thought-provoking book. Based on years of teaching experience the book provides a wealth of demonstrations, activities, examples, and projects that involve active student participation. Part I of the book presents a large selection of activities for introductory statistics courses and has chapters such as 'First week of class'-- with exercises to break the ice and get students talking; then descriptive statistics, graphics, linear regression, data collection (sampling and experimentation), probability, inference, and statistical communication. Part II gives tips on what works and what doesn't, how to set up effective demonstrations, how to encourage students to participate in class and to work effectively in group projects. Course plans for introductory statistics, statistics for social scientists, and communication and graphics are provided. Part III presents material for more advanced courses on topics such as decision theory, Bayesian statistics, sampling, and data science.

Bayesian Data Analysis describes how to conceptualize, perform, and critique statistical analyses from a Bayesian perspective. Using examples largely from the authors' own experiences, the book focuses on modern computational tools and obtains inferences using computer simulations. Its unique features include thorough discussions of the methods for checking Bayesian models and the role of the design of data collection in influencing Bayesian statistical analysis. Bayesian Data Analysis offers the practicing statistician singular guidance on all aspects of the subject.

Bayesian Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)

Carlin, John B.

Stern, Hal S.

Rubin, Donald B.

Chapman and Hall/CRC

On the night of the 2000 presidential election, Americans watched on television as polling results divided the nation's map into red and blue states. Since then the color divide has become symbolic of a culture war that thrives on stereotypes--pickup-driving red-state Republicans who vote based on God, guns, and gays; and elitist blue-state Democrats woefully out of touch with heartland values. With wit and prodigious number crunching, Andrew Gelman debunks these and other political myths.
 This expanded edition includes new data and easy-to-read graphics explaining the 2008 election. Red State, Blue State, Rich State, Poor State is a must-read for anyone seeking to make sense of today's fractured political landscape.

Red State, Blue State, Rich State, Poor State: Why Americans Vote the Way They Do - Expanded Edition

Politics & Social Sciences

Cortina, Jeronimo

Park, David K.

Shor, Boris

Sociology

Princeton University Press

Active Statistics

Vehtari, Aki

Cambridge University Press

Regression and Other Stories (Analytical Methods for Social Research)

Most textbooks on regression focus on theory and the simplest of examples. Real statistical problems, however, are complex and subtle. This is not a book about the theory of regression. It is about using regression to solve real problems of comparison, estimation, prediction, and causal inference. Unlike other books, it focuses on practical issues such as sample size and missing data and a wide range of goals and techniques. It jumps right in to methods and computer code you can use immediately. Real examples, real stories from the authors' experience demonstrate what regression can do and its limitations, with practical advice for understanding assumptions and implementing methods for experiments and observational studies. They make a smooth transition to logistic regression and GLM. The emphasis is on computation in R and Stan rather than derivations, with code available online. Graphics and presentation aid understanding of the models and model fitting.

Hill, Jennifer

Winner of the 2016 De Groot Prize from the International Society for Bayesian Analysis Now in its third edition, this classic book is widely considered the leading text on Bayesian methods, lauded for its accessible, practical approach to analyzing data and solving research problems. Bayesian Data Analysis, Third Edition continues to take an applied approach to analysis using up-to-date Bayesian methods. The authors―all leaders in the statistics community―introduce basic concepts from a data-analytic perspective before presenting advanced methods. Throughout the text, numerous worked examples drawn from real applications and research emphasize the use of Bayesian inference in practice. New to the Third Edition <ul>
<li>Four new chapters on nonparametric modeling</li> <li>Coverage of weakly informative priors and boundary-avoiding priors</li> <li>Updated discussion of cross-validation and predictive information criteria</li> <li>Improved convergence monitoring and effective sample size calculations for iterative simulation</li> <li>Presentations of Hamiltonian Monte Carlo, variational Bayes, and expectation propagation</li> <li>New and revised software code</li> </ul> The book can be used in three different ways. For undergraduate students, it introduces Bayesian inference starting from first principles. For graduate students, the text presents effective current approaches to Bayesian modeling and computation in statistics and related fields. For researchers, it provides an assortment of Bayesian methods in applied statistics. Additional materials, including data sets used in the examples, solutions to selected exercises, and software instructions, are available on the book’s web page.

Dunson, David B.

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