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Introduction to Linear Regression Models
March 17 and 19, 2015 (two days) 4:30 p.m. to 6:30 p.m., 2011 Donald Bren Hall - UC Irvine
Course Description: This is an introductory course to linear regression analysis/modeling. The method is fundamental to scientific research across disciplines. The course covers linear regression models and their applications, with emphasis in biology, medicine, and health-related studies. Further, it provides the required skills to fit a regression model to observed data, interpret its results, evaluate its performance, and examine the appropriateness of its assumptions. Illustrative examples will be implemented in the statistical software R, but no coding experience is required.
Learning Objectives: Course registrants will have the opportunity to learn and understand:
Topics: Simple linear regression models • Multiple linear regression models • Estimation • Hypothesis testing • Model selection • Model assessment • Model diagnostics
Prerequisites: Introductory level statistics and basic math and computer skills. Participants need to bring their laptops with R and R-Commander installed. Instructions for installing these programs will be provided prior to the class.
References Book: Biostatistics with R, An Introduction to Statistics Through Biological Data, Babak Shahbaba, Springer, 2012; available for download (free-of-charge) through the UCI library. Supplementary lecture notes will be provided.
Registration Fee: UCI faculty and staff: $75, UCI student: $50, non-UCI registrant: $150 (fee includes lecture notes)
Contact: Erika Whitton • (714) 456-2308 • firstname.lastname@example.org
About the Course Instructor: Dr. Babak Shahbaba is an Associate Professor of Statistics and a faculty member of the Biostatistics, Epidemiology & Research Design (BERD) Unit, UC Irvine Institute of Clinical and Translational Science. His current research focus is on statistical methods for high-dimensional problems and has expertise in the application of novel statistical methods to address research questions in genetics, proteomics, and neuroscience. Dr. Shahbaba’s research works are published in leading peer-reviewed journals, including Statistics in Medicine, Neural Computation, Psychometrika, Bayesian Analysis, Journal of the Royal Statistical Society, and Journal of Machine Learning Research.