NEW ONLINE COURSE: "Applied Quasi-Least Squares Regression" extends longitudinal methods for the analysis of correlated data.
Examples of correlated data include, but are not limited to, clustered data, repeated observations, longitudinal data, multiple dependent variables, spatial data or data from population pharmacokinetic/pharmacodynamic studies. Extra emphasis will be given to engaging in various types of modeling projects, with class and instructor discussion regarding the most appropriate ways to select an appropriate model for a given data situation, methods of constructing a model, interpreting a model, and evaluating a model for its comparative fit. “Quasi Least Squares Regression” will be taught August 24 – September 21 at The Institute for Statistics Education at Statistics.com. The instructors are Dr. Joseph Hilbe and Dr. Justine Shults, and the course will be based on their forthcoming book on the subject.
Related Upcoming courses:
Aug 3: Regression Analysis (ordinary least squares)
Aug 24: Quasi Least Squares Regression (this notice)
Aug 31: Modeling in R
Sep 7: Logistic Regression
Joseph Hilbe, elected ASA Fellow and the author of “Logistic Regression Models,” and also “Generalized Linear Models and Extensions” and “Generalized Estimating Equations,” was, until recently, the software reviews editor for “The American Statistician.”
Justine Shults, Associate Professor of Biostatistics at the University of Pennsylvania, is a co-investigator on studies in pediatrics, nutritional epidemiology, and psychiatry.
Hilbe and Shults are co-authors of the forthcoming CRC text "Quasi Least Squares Regression."
Participants can ask questions and exchange comments directly with Drs. Hilbe and Shults via a private discussion board throughout the period.
As with all online courses at Statistics.com, there are no set hours when participants must be online; course work requires about 15 hours per week.
For details and registration:
http://www.statistics.com/qls-regression/#syllabus
Peter Bruce
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