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

Office hours survey


  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. Regression Analysis:

    • Lecture: Linear regression and model diagnostics

    • Live Coding: Fitting and diagnosing regression models in R

    • Worksheet

  8. Generalized Linear Models and Mixed Models:

    • Lecture: Introduction to GLMs and mixed models

    • Live Coding: Implementing GLMs and mixed models in R

    • Worksheet

  9. Model Selection:

    • Lecture: Model selection

    • Live Coding: Applying model selection techniques in R

  10. Dimensional Reduction:

    • Lecture: PCA

    • Live Coding: PCA

  11. Advanced Topics and Applications:

    • Lecture: Bayesian methods and MCMCs

    • Live Coding: Diagnosing MCMCs

    • Worksheet

  12. Test week 2

    • Review

    • Test

Additional topics will be added as time allows.

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