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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.
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.
Additional text
"Gelman and Nolan have constructed a tour de force of clever demonstrations that will permit all who use them to communicate more effectively many of the deepest ideas of statisitical thinking."
