Teaching Statistics: A Bag of Tricks

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

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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
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About the author (2017)

Andrew Gelman is Professor of Statistics and Professor of Political Science and Director of the Applied Sciences Center at Columbia University. He has published over 250 articles in statistical theory, methods, and computation, and in applications areas including decision analysis, survey sampling, political science, public health, and policy. Deborah Nolan is Professor of Statistics at the University of California, Berkeley. Her research has involved the empirical process, high-dimensional modeling, and, more recently, technology in education and reproducible research.

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