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 Kirsten Eilertson (Kirsten.Eilertson@colostate.edu)Â for information about this course.
Prerequisite
MATH 369 (Linear Algebra I); STAT 315 (Intro to Theory and Practice of Statistics); Admission to the Master of Applied Statistics or admission to the Graduate Certificate in Theory and Applications of Regression Models. Written consent of instructor.
Important Information
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 stats_ddp@mail.colostate.edu. 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.
