Sumários

Lab 6 and Lab 7

7 dezembro 2021, 12:30 • António Sérgio Constantino Folgado Ribeiro

Lab 6 - Linear regression assumptions - conclusion
Lab 7 - Logit/Probit models

Lab 6

3 dezembro 2021, 16:30 • António Sérgio Constantino Folgado Ribeiro

Linear Regression Assumptions

Lab 6 — Testing the Linear Regression Assumptions

30 novembro 2021, 16:00 • Hugo Castro Silva

Use Monte Carlo simulation to see what happens when the linear regression assumptions are violated:
  • non-linearity
  • non-random samples
  • no variation of x, perfect linear correlation, multicollinearity
  • no conditional independence, and omitted variable bias
  • heteroskedasticity

Ch. 6 — Causality and Regression

30 novembro 2021, 14:00 • Hugo Castro Silva

Causality and Regression

  • Why is finding the causal effect important?
  • Why is finding the causal effect hard?
  • Deriving the causal effect.
    • selection bias
  • Random assignment, experiments, and the causal effect.
  • Regression analysis for experiments.
  • When is regression causal?
    • sources of selection bias.
  • Omitted variable bias.
    • finding the sign of the bias in simple linear regression.
    • intuition for multiple linear regression.
    • possible solutions for dealing with omitted variable bias.

Lab 6

30 novembro 2021, 12:30 • António Sérgio Constantino Folgado Ribeiro

Linear Regression Assumptions