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.
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.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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