Reading materials | Weekly Q&A | Lecture notes

Key concepts

  • Dependent and independent variable (missing reference)
  • Slope (regression coefficient) and intercept (missing reference)
  • Goodness of fit and outliers (missing reference)
  • Residual variation and standard error of the estimate (missing reference)
  • Coefficient of determination r-squared (missing reference)
  • Standard error of the slope (missing reference)
  • Assumptions of linear regression (missing reference)
    • Errors Normally distributed
    • Homoskedasticity
    • Errors are independent of each other
    • Independent and dependent variables must be interval variables
    • Linear relationship between variables
  • Ordinary least squares (OLS) (Allison, 1999)
  • Partial slope (regression coefficients) (missing reference)
  • Multiple coefficient of determination R-squared (missing reference)
  • Adjusted R-squared (missing reference)
  • Dummy variable regression (missing reference)
  • Additional regression assumptions (missing reference)
    • Model is specified correctly
    • Low multi-collinearity
  • Polynomial curve fitting (missing reference)

Before class

Review the key concepts from previous two weeks. Prepare xxx.

In class

  • Concepts review section.
  • Lecture on applications with real examples.
  • “Fake with GPT”

After class