Foundations of Agnostic Statistics

Provides an introduction to modern statistical theory for social and health scientists while invoking minimal modeling assumptions.

List of contents

Introduction; Part I. Probability: 1. Probability theory; 2. Summarizing distributions; Part II. Statistics: 3. Learning from random samples; 4. Regression; 5. Parametric models; Part III. Identification: 6. Missing data; 7. Causal inference.

About the author

Peter M. Aronow is an Associate Professor of Political Science, Public Health (Biostatistics), and Statistics and Data Science at Yale University, Connecticut and is affiliated with the University's Institution for Social and Policy Studies, Center for the Study of American Politics, Institute for Network Science, and Operations Research Doctoral Program.Benjamin T. Miller is a doctoral candidate in Political Science at Yale University, Connecticut. In 2012, Mr Miller received a B.A. in Economics and Mathematics from Amherst College.

Summary

This book provides a rigorous but accessible treatment of modern statistical methodology for researchers in the social and health sciences. It provides readers with the mathematical tools to critically engage with cutting-edge statistical methods.

Product details

• Authors: Peter M Aronow, Peter M. Aronow, Peter M. (Yale University Aronow, Benjamin T Miller, Benjamin T. Miller, Benjamin T. (Yale University Miller
• Publisher: Cambridge University Press ELT
• Languages: English
• Product format: Paperback / Softback
• Released: 31.01.2019
• EAN: 9781316631140
• ISBN: 978-1-316-63114-0
• No. of pages: 314
• Subjects: Natural sciences, medicine, IT, technology > Mathematics > Probability theory, stochastic theory, mathematical statistics; Social sciences, law, business > Social sciences (general); Epidemiology & medical statistics, POLITICAL SCIENCE / General, Social research & statistics, Probability & statistics, Probability and statistics, Social research and statistics, Epidemiology and Medical statistics