Linear Regression

• 1. Ordinary Least Squares
  • 1.1. Learning objectives
  • 1.2. Introduction
  • 1.3. Data scaling
  • 1.4. OLS regression
  • 1.5. Uncertainty propagation
  • 1.6. Visualizing relationships
  • 1.7. Point estimates
  • 1.8. Summary
• 2. Weighted Least Squares
  • 2.1. Learning objectives
  • 2.2. Introduction
  • 2.3. WLS regression
  • 2.4. Uncertainty propagation
  • 2.5. Summary
• 3. Bayesian Parameter Estimation I
  • 3.1. Learning objectives
  • 3.2. Introduction
  • 3.3. Slope and intercept estimation
  • 3.4. Highest Density Interval (HDI)
  • 3.5. Fit comparison
  • 3.6. Parameter estimation on real and simulated data
  • 3.7. Summary
• 4. Bayesian Parameter Estimation II
  • 4.1. Learning objectives
  • 4.2. Introduction
  • 4.3. Slope and intercept estimation
  • 4.4. Plotting
  • 4.5. Summary
• 5. Case Study: Absorption Coefficient