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