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 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. For more details please visit at http://www.statistics.com/qls-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."
Hilbe and Shults are co-authors of the forthcoming CRC text "Quasi Least Squares Regression."
- Use of QLS in biomedical research and business analytics
- How QLS compares with some popular competing approaches, including mexed-effects models
- Analysis of cross-sectional and clustered data (e.g. clinical data that are collected on a family who are measured at one time point; customer satisfaction scores that are measured in different departments of a store during a one-day sale)
- Analysis of equally-spaced longitudinal data (e.g. clinical trials with measurements collected at baseline and at 6 and 12 months; monthly hospital pricing data)
- Analysis of unequally-sapced longitudinal data (e.g. clinical trials with unequally-spaced measurements)
- Analysis of clustered data measured over time, such as
- Adverse events measured on different body systems of patients at several measurement occasions
- Montly customer satisfaction scores in several departments of a store
- Choice of variables to include in the regression model, e.g. age, gender, income, severity of disease...
- Adequacy of assumed correlation structure
- Sensitivity of results when compared with fitting other popular approaches such as mixed models