The true power of analytics comes from looking at the relationships among multiple variables.
We will examine how two variables can go together with correlation
We will also examine how to use one variable to predict another variable using regression
Class Data Demos on Google Sheets
Complete assigned readings before next class meeting, which are yellow-highlighted.
DA09 Correlation (continued)
- Required Reading: Machine Learning Predicts Home Price
- Netflix Prize (2009)
- JASP software: Find correlation and regression tutorials here
- Simple Linear Regression (Penn State)
- Ordinary Least Squares Regression Explained Visually
- Reading: A Refresher on Regression Analysis (Harvard Business Review)*
- Khan Academy module on Inference about Slope
- [MINI] Ordinary Least Squares Regression by Data Skeptic podcast
- [MINI] Noise! by Data Skeptic podcast
- Ordinary Least Squares: Where It All Began by Quantitude Podcast
- The Question of When: The Oscars, Class Presentations, and Prom Dates
DA10 Regression Part 1 of 2: PPT
- NFF Chapter 11: Correlation and linear regression (pdf) - focus on the model evaluation section, starting with section 11.9
- NFF Chapter 12: Comparing several means (one-way ANOVA) (pdf)
- Example Study: A Survey of Causal Inference Applications at Netflix
- Example Study: Supercharging A/B Testing at Uber
- Example Study: Dan Ariely: How to change your behavior for the better - TED
- A Visual Introduction to Linear Regression (by Amazon's Machine Learning University)
- [MINI] R-squared by Data Skeptic podcast
- F-Distribution Tables
- Chapter 6 ANOVA from Introductory Business Statistics
- DA Worksheet #08: Regression 1 of 2 (Solution; Python Solution)
- Zillow Prize (from 2018)
- Khan Academy module on ANOVA
- [MINI] ANOVA by Data Skeptic Podcast
- ANOVA lecture by Dr. Andy Field
- JASP Tutorial on ANOVA