# Teaching Statistics: A Bag of Tricks

Oxford University Press, May 4, 2017 - Mathematics - 384 pages
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.

### What people are saying -Write a review

We haven't found any reviews in the usual places.

### Contents

 1 Introduction 1 Part I Introductory probability and statistics 9 2 First week of class 11 3 Descriptive statistics 19 4 Statistical graphics 38 5 Linear regression and correlation 48 6 Data collection 58 7 Statistical literacy and the news media 90
 14 Teaching statistics to social scientists 221 15 Statistics diaries 228 16 A course in statistical communication and graphics 252 Part III More advanced courses 275 17 Decision theory and Bayesian statistics 277 18 Student activities in survey sampling 296 19 Problems and projects in probability 315 20 Directed projects in a mathematical statistics course 332

 8 Probability 117 9 Statistical inference 134 10 Multiple regression and nonlinear models 151 11 Lying with statistics 162 Part II Putting it all together 177 12 How to do it 179 13 Structuring an introductory statistics course 209
 21 Statistical thinking in a data science course 342 Notes 361 References 375 Author Index 387 Subject Index 392 Copyright