# Lecture 16 – Probability II Estimators, Bias, and Variance

Presented by Anirudhan Badrinath and Dominic Liu

Content by Fernando Pérez, Alvin Wan, Suraj Rampure, Allen Shen, Joseph Gonzalez, Andrew Bray, Josh Hug, Lisa Yan, Ani Adhikari, and Sam Lau

**IMPORTANT: Error in Lecture 16 Slide 28**

**X is not random on Slide 28 because of ϵ (shouldn’t be blue)! The explanatory variable (independent variable) is a constant value unaffected by measurement noise, as mentioned in previous slides. Later on, we discuss that the expectation in model risk, bias, variance, etc. is across all possible samples of X in the data distribution, but that’s not yet!**