- Revision of the basic concepts of Matrix Algebra
- The Classical Linear Regression Model: The basic assumptions
- OLS method, properties of Least Squares estimators, the Gauss-Markov theorem.
- The coefficient of determination (R2) as a measure of goodness of fit.
- Multiple linear regression models: estimation and testing (OLS estimates, maximum likelihood estimates, BLUE estimates)
- Extension of the Linear model: Non-linear models and regression on dummy variables
- Multicollinearity and Specification error
- Violation of stochastic assumptions: Violating the normality assumption and the zero mean
- Heteroskedasticity (the nature of the problem, consequences, detection and remedial measures).
- 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).
- Errors in measurements (consequences, instrumental variables)
- Using Matrix Algebra in regression