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 SAS and 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 has print-based exams that require a proctor. A Distance Proctor Form will be required. Electronic proctoring is not available for this.
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
Jana Anderson is an associate professor and the director of the Online Learning and Master of Applied Statistics programs in the Department of Statistics at Colorado State University. Prof. Anderson joined the Department of Statistics in 1994. She is a member of the American Statistical Association and is active in the Statistical Education Section. She received her Ph.D. in Statistics from Colorado State University.