Drs. Brian Everitt and Torsten Hothorn will present their online course "Modeling in R" at statistics.com Jan. 19 - Feb. 16. Participants can ask questions and exchange comments with Drs. Everitt and Hothorn via a private discussion board throughout the period.
In this course you learn how to use R to build statistical models and use them to analyze data. Multiple regression is covered first, then logistic regression and the generalized linear model (multiple regression and logistic regression illustrated as special cases). The Poisson model for count data, and the concept of overdispersion are also covered. You learn how to analyze longitudinal data using straightforward graphics and simple inferential approaches, then mixed-effects models and the generalized estimating approach for such data. The course emphasizes how to fit the models listed and interpret results, rather than how to derive the theoretical background of the models. Brian Everitt and Torsten Hothorn are the authors of "A Handbook of Statistical Analyses Using R." Brian Everitt is Professor Emeritus, King's College, London, and author of more than 50 books on statistics, including "Applied Multivariate Analysis" and "Statistical Aspects of the Design and Analysis of Clinical Trials." Torsten Hothorn is Lecturer of Statistics at the Institut fur Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universitat, Erlangen-Nurnberg, Germany, and the author of over 4 dozen scholarly papers in peer-reviewed journals and other publications. Details/prerequisites: http://www.statistics.com/courses/modelingr/ The course takes place online at statistics.com in a series of 4 weekly lessons and assignments, and requires about 7-15 hours/week. Participate at your own convenience; there are no set times when you are required to be online. Peter Bruce [EMAIL PROTECTED] ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
