This is a graduate level course in analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated measures designs, crossover designs and analysis of covariance (ANCOVA). This course is cohort-based, which means that there is an established start and end date, and that you will interact with other students throughout the course.
This is a course and analysis of variance (ANOVA), including randomization and blocking, single and multiple factor designs, crossed and nested factors, quantitative and qualitative factors, random and fixed effects, split plot and repeated measures designs crossover designs and analysis of covariance (ANCOVA).
This graduate level course covers the following topics:
- Understanding the contexts in which ANOVA is appropriate
- Using the notation and formulas used to compute values fundamental to ANOVA
- Understanding the statistical model for analysis of variance and the relationship between ANOVA and regression
- How to implement single factor or One-way ANOVA analyses
- How to implement multiple factor ANOVA analyses
- Extending treatment designs to include random effects
- Understanding the importance that experimental design and randomization has in being able to interpret results
- Understanding the structure of split-plot ANOVA designs and analysis
- How to include a continuous covariate variable in ANOVA (ANCOVA)
- Understanding the use of the General Linear Model
- Fitting and testing polynomial models for a quantitative factor
- Recognizing and analyzing repeated measures designs
- Recognizing and analyzing cross-over repeated measures designs
Dr. Durland Shumway is the author of these course materials and has taught this course for many semesters in residence and more recently online.
This course uses Examity for proctored exams. For more information view O.3 What is a proctored exam? in the student orientation.
Students will use both SAS and Minitab. If you are taking additional upper level STAT courses we recommend that you purchase a permanent license for Minitab. See the Statisitical Software page for more information about accessing these applications. PLEASE NOTE: The Minitab v. 14 Student Version does not have full statistical functionality and is not recommended for STAT courses.
Minitab and SAS will be supported. Sample programs will be supplied but students will be required to do some programing on their own. Students should already feel comfortable using either Minitab and SAS, or be a quick learner of software packages, or be able to figure out how to do the required analyses in another package of their choice. Due to different software versions and platforms there may be issues with running a code. Students should NOT wait to the point of frustration but must be proactive in seeking advice and help from appropriate sources including documentation resources, other students via the online discussion boards, the teaching assistant, instructor or helpdesk.
Students who have no experience with programming or are anxious about being able to manipulate software code are strongly encouraged to take the one-credit course in SAS in order to establish this foundation before taking STAT 502.
There are two options of textbooks for this course. Students may use either:
The larger Applied Linear Statistical Models by Kutner, Nachtsheim, and Neter (5th edition) OR the smaller Analysis of Variance, a custom printing of the second half of the larger text (ISBN-97811216693-76).
Students may use either textbook listed.
The first half of the larger Applied Linear Statistical Models contains sections on regression models, the second half on analysis of variance and experimental design. The first 12 chapters on regression models are not covered in STAT 502, however these topics are covered in STAT 501 where these chapters are required. Students may consider purchasing the larger text if they are taking both courses. Applied Linear Statistical Models is considered to be one of the "bibles" of applied statistics so it probably will have value to you beyond this course.
STAT 501, Basic knowledge of Minitab and SAS is recommended.