1. INTRODUCTION.
** 1.1 Formulation and meaning of a statistical regression problem.
** 1.2 Regression models. Goals of a regression analysis.
** 1.3 Data in a regression analysis.
** 1.4 Regression software.
2. THE MULTIPLE LINEAR REGRESSION MODEL: ESTIMATION.
** 2.1 Definition and matrix expression.
** 2.2 Least squares estimation.
** 2.3 Analysis of variance.
Appendix:
** A.1 The multivariate normal distribution.
3.THE MULTIPLE LINEAR REGRESSION MODEL: HYPOTHESIS
TESTING AND CONFIDENCE REGIONS.
** 3.1 The F-test for the general linear hypothesis.
** 3.2 Confidence regions.
** 3.3 Prediction intervals.
Appendix:
** A.1 Indicator variables.
4. MULTICOLLINEARITY, RESIDUAL ANALYSIS, AND DIAGNOSTIC
TECHNIQUES.
** 4.1 Multicollinearity: description and consequences.
** 4.2 Residual analysis.
** 4.3 Outliers and extreme cases.
5. GENERALIZED LEAST SQUARES THEORY.
** 5.1 Cases of known and unknown covariance matrix.
** 5.2 Heteroscedasticity.
** 5.3 Transformations.
** 5.4 Serial correlation.
6. TIME SERIES MODELS
** 6.1 Autoregressive (AR) and moving average (MA) models.
** 6.2 ARMA and ARIMA models.
