Includes bibliographical references and index.

Introduction -- Analysis of a sample of data -- Analysis of a sample from a normal population -- Interval estimation and hypothesis testing in the normal linear model -- The Bayesian approach to estimating the mean and variance of a normal population -- The general linear statistical model -- The normal general linear statistical model -- Bayesian estimation and interference for the normal linear statistical model -- Linear stochastic regressor models and asymptotic theory -- General linear statistical model with non scalar-identity covariance matrix -- Disturbance-related sets of regression equations -- An introduction to simultaneous linear statistical models -- Estimation and interference for simultaneous equation statistical models -- Heteroscedasticity -- Autocorrelation -- Using time series and cross-sectional data -- Variable parameter models -- Models with qualitative or limited dependent variables -- Unobservable variables -- The use of nonsample information -- Biased estimation -- Model specification--variable selection -- Multicollinearity -- Nonlinear regression models -- Time series analysis and forecasting -- Analysis of bivariate time series -- Distributed lag models -- Summary of statistical models, estimators and tests.