Alan Agresti, Ph.D.

Distinguished Professor Emeritus

Statistics

Categorical Data Analysis; Generalized Linear Models; Social Statistics; Biostatistics

Dates of Service: 1972–2010

Honors

  • Fellow, American Statistical Association
  • Fellow, Institute of Mathematical Statistics
  • Honorary doctorate, De Montfort University (Leicester, U.K.), 1999
  • Statistician of the Year, Chicago chapter of American Statistical Association, 2003
  • Recipient of the first Herman Callaert Leadership Award in Statistical Education and Dissemination, Hasselt University, Belgium, 2004

Education

  • B.A. University of Rochester, 1968
  • Ph.D. University of Wisconsin, 1972
  • Honorary Doctor of Science, De Montfort University (Leicester, U.K.), 1999

Other Information

The text Statistics: The Art and Science of Learning from Data (5th edition, 2021) written with Christine Franklin and Bernhard Klingenberg, is designed for a one-term or two-term course on an introduction to statistics presented with a conceptual approach. More information, including a link to the Table of Contents and sample chapters, is available at my personal home page. My text Categorical Data Analysis (third edition, 2012) is designed for a masters-level course on this topic. My personal home page, listed in the heading above contains a link to a website for the text that includes datasets, some solutions, and software information. An Introduction to Categorical Data Analysis (3rd edition, 2019) presents a nontechnical introduction to topics such as logistic regression and loglinear models. For files containing data sets from this text, go to my personal home page. Statistical Methods for the Social Sciences (5th edition 2018) is designed for a two-semester sequence in statistical methods. It begins with the basics of statistical description and inference, and the second half of the book concentrates on regression methods, including multiple regression, ANOVA and repeated measures ANOVA, analysis of covariance, logistic regression, and generalized linear models. For a file containing some of the large data sets from the text, go to my personal home page, which also has information about other books, including Foundations of Linear and Generalized Linear Models (2015) and Foundations of Statistics for Data Scientists, With R and Python (with Maria Kateri, 2022).