[OCRUG X-Post] Modeling Normally Distributed Data with Repeated Measures
Cross-post for OCRUG:
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Develop the practical skills and foundation knowledge to effectively use some of the most common regression models used by data scientists.
About this Event
OCRUG understands that these are extraordinary times and we endeavour to keep our events free or very low cost. If you would like to attend the event but the registration fee would put a financial strain on you, please reach out to firstname.lastname@example.org. There are a limited number of complimentary tickets available.
This workshop will give you the practical skills and foundational knowledge to effectively use some powerful regression models used by data scientists. When data are collected on the same subjects repeatedly over time (for example, in clinical trials or cohort studies) or under different conditions (for example, in a designed experiment), the measurements within the same individual are modeled as having correlated values. At the workshop, we will consider several models that can be employed to model a normally distributed response variable. The models that we will consider are: random slope and intercept (mixed-effects) model and generalized estimating equations models with unstructured, autoregressive, compound symmetric (exchangeable), and independent working correlation matrices. All models will be run in R version 4.0.3.
The course will be structured as follows. For each part, we will first discuss the theory, then work through an example. After that, the participants will work in small groups in break-out rooms to do hands-on exercises to help reinforce the material. All the files and Rstudio will be made available to the participants.
We would like to use the RStudio Cloud. If you are not familiar with this technology, the participants use a web browser to access RStudio. The environment will be setup and loaded with the code and data that is needed. This way, participants can focus on building models.
The material covered by the workshop will be taken from my recently published book “Advanced Regression Models with SAS and R Applications” (https://www.amazon.com/dp/1138049018/), CRC Press, 2018.
# About the Instructor
Dr. Olga Korosteleva, is a professor of Statistics at the Department of Mathematics and Statistics at California State University, Long Beach (CSULB). She received her Bachelor’s degree in Mathematics in 1996 from Wayne State University in Detroit, and a Ph.D. in Statistics from Purdue University in West Lafayette, Indiana, in 2002. Since then she has been teaching mostly Statistics courses in the Master’s program in Applied Statistics at CSULB, and loving it!
Dr. Olga is an undergraduate advisor for students majoring in Mathematics with an option in Statistics. She is also the faculty supervisor for the Statistics Student Association. She is also the immediate past-president of the Southern California Chapter of the American Statistical Association (SCASA). Dr. Olga is the editor-in-chief of SCASA’s monthly eNewsletter and the author (co-author) of four statistical books.
- 06:30-06:40 Introduction
- 06:40-07:30 Mixed-effects Model for Normal Response
- 07:30-07:50 Mixed-effects Model Exercise
- 07:50-08:00 Mixed-effects Model Solution
- 08:00-08:10 Break
- 08:10-08:30 Generalized Estimating Equations (GEE) Model for Normal Response
- 08:30-08:50 GEE Exercise
- 08:50-09:00 GEE Solution
- 09:00-09:30 Additional Exercise and Solution
- 09:30-09:45 Wrap up
This event is sponsored by the University of California, Paul Merage School of Business. https://merage.uci.edu/
# Code of Conduct