STAT 505 is the first semester in a two semester sequence of courses designed to help you gain a deeper level of understanding of the most common statistical methods used by statisticians. STAT 505 is a course in the theory and application of linear models, the foundation of most models used in statistical analysis. Topics include: Special matrix theory for statistics, multivariate normal distribution, distributions of quadratic forms, estimation and testing for the general linear model, one-way and two-way classification models, contrasts (main effect, simple effect and interaction), and multiple comparison techniques.

By the end of this course, the successful student should be able to:

  1. Use rigorous mathematical techniques and methods of proof to derive results in matrix theory for statistics.
  2. Derive estimators, hypothesis tests, and confidence intervals related to linear models.
  3. Understand the assumptions, uses, and limitations of linear models.
  4. Use R to fit linear models to data and conduct inference on linear model parameters, with and without using built-in functions.
  5. Communicate (written, visually, and orally) results of a statistical analysis and statistical concepts in non-technical terms.

Course Information

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Data Sets

R References

Links to additional R Resources are posted in our D2L Content page.