1. Revision of the basic concepts of Matrix Algebra
  2. The Classical Linear Regression Model: The basic assumptions
  3. OLS method, properties of Least Squares estimators, the Gauss-Markov theorem.
  4. The coefficient of determination (R2) as a measure of goodness of fit.
  5. Multiple linear regression models: estimation and testing (OLS estimates, maximum likelihood estimates, BLUE estimates)
  6. Extension of the Linear model: Non-linear models and regression on dummy variables
  7. Multicollinearity and Specification error
  8. Violation of stochastic assumptions: Violating the normality assumption and the zero mean
  9. Heteroskedasticity (the nature of the problem, consequences, detection and remedial measures).
  10. Autocorrelation (the nature of the problem, consequences of using OLS in the presence of autocorrelation, detecting autocorrelation, remedial measures when the structure of autocorrelations is known and when ρ is not known).
  11. Errors in measurements (consequences, instrumental variables)
  12. Using Matrix Algebra in regression