STAT 870 - Analysis of Messy Data

Fall 2025
Kansas State University

Instructor: Dr. Josefina Lacasa

Time: Mon-Wed 11:30 am - 12.45 pm

Course Description: This course is designed for students who already understand the fundamental principles of designed experiments and want to deepen their knowledge by learning how to analyze data from more complex designed experiments than those covered in introductory courses. Topics to be covered include but are not limited to treatment design and experiment design, generalized linear mixed models, generalized additive models, how to write out a statistical model that corresponds to the design, multiple comparison procedures, and spatial corrections for the analysis of field experiments. We may also cover missing data problems, power analyses, and what to write the materials section in a paper. Some topics may be covered under a Bayesian approach. This course includes a semester project to be discussed between the instructor and the student.

Main Goal of this Course:
The main objective of this course is to learn a practical way to modeling data generated from designed experiments, interpreting the results and making inference.

What this course is not: an R programming course (however, help will be provided).

Prerequisites: STAT 720.