Explains the least-squares regression line, prediction errors, RMSE, and numerical optimization for finding the best linear fit.
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Berkeley Data 8: Foundations of Data Science
All
- 01-Introduction
01-Introduction
- 02-Cause and Effect
- 03-Tables
- 04-Data Types
- 05-Building Tables
- 06-Census
- 07-Charts
- 08-Histograms
- 09-Functions
- 10-Groups
- 11-Pivots and Joins
- 12-Table Examples
- 13-Conditionals and Iteration
- 14-Chance
- 15-Sampling
- 16-Models
- 17-Comparing Distributions
- 18-Decisions and Uncertainty
- 19-A/B Testing
- 20-Causality
- 21-Examples
- 22-Midterm Review
- 23-Confidence Intervals
- 24-Interpreting Confidence
- 25-Center and Spread
- 26-The Normal Distribution
- 29-Correlation
- 30-Linear Regression
- 31-Least Squares
- 32-Guest Lecture: DJ Patil
- 33-Residuals
- 34-Regression Inference
- 35-Updating Probabilities
- 36-Classification
- 37-Classifiers
- 38-Case Study: Tech