Estimation and hypothesis testing methods in linear models, including t-tests, ANOVA, regression, and multiple regression. Residual analyses, transformations, goodness of fit, interaction and confounding. Implementation in R. Requires a background in calculus, linear algebra, inferential statistics and R computing. Students are required to take GSLL 3095 and GSLL 3096 before taking this class.
Students may contact Jana.Anderson@colostate.edu or by phone at (970) 491-7454 for information about this course.
This course can be applied toward:
Admission to the Master of Applied Statistics or admission to the Graduate Certificate in Theory and Applications of Regression Models or written consent of instructor. This is a partial-semester course.
Tuition includes access to lecture recordings which are available by streamed video. Lecture recordings may also be available by download or on DVD. To determine viewing options, contact the Department of Statistics degree program staff at firstname.lastname@example.org. Visit the Department of Statistics website to learn more about what to do after registration, including creating your eID (if necessary) and accessing your course.
Textbooks and Materials
Textbooks and materials can be purchased at the CSU Bookstore unless otherwise indicated.
- Applied Regression Analysis, 3rd Ed.
Draper & Smith
Dr. Shaby is an Associate Professor of Statistics at Colorado State University who develops statistical theory and methods to study extreme weather events and high-throughput biological experiments. He works with climate scientists, hydrologists, and wildfire scientists in academia and government to understand and mitigate the risks associated with rare, high-impact events. Dr. Shaby was the recipient of a National Science Foundation CAREER award in 2018 and the American Statistical Association Section on Statistics and the Environment's Early Career Investigator Award in 2016. He completed his Ph.D. at Cornell University in 2009 and held postdoctoral appointments at Duke University and UC Berkeley.