Experimental Design

Learning Objective

This course is intended to provide a foundation in the proper design of scientific research projects in the field of biology.

By the end of the course, successful students will be able to:
  1. Design studies that are statically tractable
  2. Graphically explore data in R
  3. Perform standard statistical analyses in the R environment


Class meets MW 4:00-5:15 in HELD 200
For more information email Dr. Blackmon.

Syllabus

  1. Introduction:

    • Lecture: Why take this course?
      PDF
      PPT

    • Lecture: Effective use of AI

    • Worksheet 1: Who are you and why are you here

  2. Basic R:
  3. Statistical Principles and a last bit of visualization theory:
  4. Probability and Discrete Variables:
  5. Hypothesis Testing 1:
  6. Hypothesis Testing 2 and Review:
  7. Test / Regression Analysis:

    • Lecture: Linear regression and model diagnostics

      Test is up on Canvas
      Use this file for the midterm: gnathocerus.csv

  8. Generalized Linear Models (and more linear regression stuff):
  9. Model Selection:
  10. GWAS, Random Effects, Monte Carlo:
  11. Bayesian and MCMC:
  12. Advanced Topics and Applications:
  13. Final Exam


Extra Resources

Examples
R markdown
R style guide
basics of R cheat sheet
base R plotting cheat sheet
ggplot2 cheat sheet
color brewer


Useful R packages

swirl
ggplot2
coda