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 requires proctored exams. Details will be provided in the course syllabus.
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
Jana Anderson is a professor in the Department of Statistics at Colorado State University. She is the director for the Master of Applied Statistics Program and also directs the Statistics Department’s certificate programs and online learning program. She joined the Statistics faculty in 1994, having earned her bachelor’s degrees from Southern Methodist University and her MS and PhD degrees from Colorado State University. Her interests include statistics/data science education and teaching with technology. Her recent contributions have involved developing a data science specialization for the Master of Applied Statistics degree program, as well as developing graduate level service courses in specialized areas.