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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.
List of contents
- 1: Introduction
- Introductory probability and statistics
- 2: First week of class
- 3: Descriptive statistics
- 4: Statistical graphics
- 5: Linear regression and correlation
- 6: Data collection
- 7: Statistical literacy and the news media
- 8: Probability
- 9: Statistical inference
- 10: Multiple regression and nonlinear models
- 11: Lying with statistics
- Putting it all together
- 12: How to do it
- 13: Structuring an introductory statistics course
- 14: Teaching statistics to social scientists
- 15: Statistics diaries
- 16: A course in statistical communication and graphics
- More advanced courses
- 17: Decision theory and Bayesian statistics
- 18: Student activities in survey sampling
- 19: Problems and projects in probability
- 20: Directed projects in a mathematical statistics course
- 21: Statistical thinking in a data science course
About the author
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.
Summary
To help overcome the challenges of teaching statistics across various diciplines, Gelman and Nolan have put together this fascinating and thought-provoking book based on years of teaching experience.
