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Course Policies for STAT 5932
Applied Mixed Models

• Course Description: Mixed models are a powerful conceptual tool for analyzing data arising from agricultural, biological, and environmental experiments. This is especially the case when data is collected over time or space. Moreover, recent advances in statistical software packages have helped make mixed models accessible. In this course, a general approach to using mixed models will be developed. However, the focus will be on applications and, in particular, the implementation of mixed model methodology in two popular statistical software packages; namely, SAS and R. Knowledge of basic statistical thinking such as that encountered in STAT 5021 is required but no prior knowledge of either SAS or R will be presumed. A partial list of possible course topics include
• Introduction to SAS and R.
• Graphical methods in SAS and R.
• Linear Regression and ANOVA (review)
• Logistic and Poisson Regression (binary and count data)
• Methods for correlated data
• Linear Mixed Models (normally distributed data)
• Generalized Linear Mixed Models (non-normal data)
• Repeated measures/ longitudinal data
This list is flexible and will be adjusted to meet the needs of those in the class.
• Required Text: None as I am unaware of a single text that is a good fit for the course. Here are a few references that I find useful.
• Introductory Statistics with R
• Mixed Effects Models in S and S-Plus
• SAS System for Elementary Statistical Analysis, Second Edition
• SAS for Mixed Models, Second Edition
• Linear Mixed Models for Longitudinal Data
• Models for Discrete Longitudinal Data
• Lectures: Tuesday and Thursday from 8:45 A.M. -- 9:55 A.M. (This will be adjusted after the first day of class.) in Vocational - Technical Education R285.

• Computing: As this course is oriented towards applied statistics, the computing requirements are substantial. The default computing packages will be R and SAS. Only the use of R and SAS will be covered in class.

• Homework: There will be a several data analysis projects. I encourage students to work together on homework assignments. However, do not simply copy someone else's work. The work you turn in must be your own.
• Project:Each student will choose a project in consultation with the instructor. For many students this will be a substantial data analysis project using the methods covered in this course but there other options as well.

• Exams and Quizzes: None.

• Grading:
  • The homeworks and project will form the basis for assigning a grade and S/U grading will be permitted.

• Uniform Drop/Add Requirements: See University policy via the following link: Uniform drop/add requirements.

• Incompletes: University policy states: "There shall be a temporary symbol I, incomplete, awarded to indicate that the work of the course has not been completed. The I shall be assigned at the discretion of the instructor when, due to extraordinary circumstances, the student was prevented from completing the work of the course on time. The assignment of an I requires a written agreement between the instructor and student specifying the time and manner in which the student will complete the course requirements. In no event may any such written agreement allow a period of longer than one year to complete the course requirements." An "I" will be given only in cases of extreme hardship.

• Disability Services: We will ensure equal learning opportunities for disabled students. Talk to the instructor and Disability Services to make arrangements.