pt. 1. Single-equation regression models. The nature of regression analysis -- Two-variable regression analysis : some basic ideas -- Two-variable regression model : the problem of estimation -- Classical normal linear regression model (CNLRM) -- Two-variable regression : interval estimation and hypothesis testing -- Extensions of the two-variable linear regression model -- Multiple regression analysis : the problem of estimation -- Multiple regression analysis : the problem of inference -- Dummy variable regression models -- pt. 2. Relaxing the assumptions of the classical model. Multicollinearity : what happens if the regressors are correlated -- Heteroscedasticity : what happens if the error variance is nonconstant? -- Autocorrelation : what happens if the error terms are correlated -- Econometric modeling : model specification and diagnostic testing -- pt. 3. Topics in econometrics. Nonlinear regression models -- Qualitative response regression models -- Panel data regression models -- Dynamic econometric models : autoregressive and distributed-lag models -- pt. 4. Simultaneous-equation models. Simultaneous-equation models -- The identification problem -- Simultaneous-equation methods -- Time series econometrics : some basic concepts -- Time series econometrics : forecasting.