Class Data Demos on Google Sheets
Complete assigned readings before next class meeting, which are yellow-highlighted
W6-1 Chi-Square: PPT
- NFF Chapter 12: Comparing several means (one-way ANOVA) (pdf)
- DA Worksheet #10: Logistic Regression (Solution; Python Solution)
- A Visual Introduction to Logistic Regression (by Amazon's Machine Learning University)
- Logit on Wikipedia
- Sigmoid, the inverse of Logit, on Wikipedia
- How logarithms work
- What is e: Euler's number
- Why use odds ratios in logistic regression by Analysis Factor
- JASP Tutorial on Logistic Regression
- Logistic Regression by StatQuest
- Logistic Regression Lesson by Penn State
- Reading: Using Logistic Regression Coefficients
- Logistic Regression as a Machine Learning Algorithm by Andrew Ng
- Dealing with Imbalanced Datasets in Classification by StarQuest
- How Can You Escape Binary Thinking? (No Stupid Questions Podcast)
W6-2 Logistic Regression: PPT
W6-3.1 Multiple Regression: PPT
W6-3.2 Regression Model Diagnostics: PPT
- DA Worksheet #15: Regression Model Diagnostics (Solution; Python Solution)
- ROC & AUC (Wilber, Amazon Machine Learning University)
- Equality of Odds (Wilber, Amazon Machine Learning University)
- Reading: Transformation & Interaction
- Reading: Influential Points (Outliers)
- Reading: Regression Pitfalls
- Reading: Categorical Predictors
- Reading: Transformation & Interaction
- Reading: Nonlinear Regression
- Khan Academy module on Non-Linear Regression
- Reading: 18 Type of Regression and When to Use them
[Optional] Time Series Analysis: PPT