Teaching

Upcoming on Fall 2024 – CPSC 541- Regression Analysis

This course covers routinely used regression-based statistical analytical techniques with a focus on agricultural applications.

Note that it has been several years since this course was last taught, and it will have major updates to the syllabus and the code works.

This year, we will leverage PraireLearn and Jupyter notebooks for coding exercises, allowing both R and Python for analytics.
Although most of the techniques and topics covered here can be encountered in various other stats courses offered across campus, this course focuses on understanding and working with real-world data, specifically teaching how to understand and pick analytical methods to ask and answer your own research questions with credibility.

Topics & Techniques Covered

Non-exhaustive

  1. Sampling Distributions
  2. Common families of Statistical Distributions
  3. Linear Regression
  4. Logistic Regression
  5. Non-linear Regression
  6. Curve Fitting
  7. Variance and Variance Components
  8. Sources of Error
  9. Goodness-Of-Fit
  10. Bootstrapping and Cross Validation
  11. Ensemble Methods
  12. Decision Trees
  13. Random Forest
  14. Gradient Descent
  15. Regularization Techniques
  16. Interpolation

Tools and Platforms

Basic Python – libraries

  1. Pandas
  2. Numpy
  3. Scikit-learn
  4. SciPy
  5. Seaborn
  6. Matplotlib

Project Management Basics & Platforms

  • Google Sheets
  • Monday.com
  • SmartSheets

Collaborative Computing, Version Control & IDEs

  • Github/GitLab
  • Colab
  • Jupyter
  • PyCharm / IntelliJ / VirtualStudio
  • R-Studio