Chapter 6: Multiple Regression

6/1  Introduction to multiple linear regression
6/1/1  Multiple regression model: theory and practice
6/1/2  Solving for the regression coefficients
6/1/3  Multiple regression and the coefficient of determination
6/1/4  The F-test for overall significance
6/1/5  Individual coefficients: confidence intervals and t-tests
6/1/6  The assumptions behind multiple linear regression models
6/2  Regression with time series
6/2/1  Checking independence of residuals
6/2/2  Time-related explanatory variables
6/3  Selecting variables
6/3/1  The long list
6/3/2  The short list
6/3/3  Best subsets regression
6/3/4  Stepwise regression
6/4  Multicollinearity
6/4/1  Multicollinearity when there are two explanatory variables
6/4/2  Multicollinearity when there are more than two explanatory variables
6/5  Multiple regression and forecasting
6/5/1  Example: cross-sectional regression and forecasting
6/5/2  Example: time series regression and forecasting
6/5/3  Recapitulation
6/6  Econometric models
6/6/1  The basis of econometric modeling
6/6/2  The advantages and drawbacks of econometric methods
Appendixes
6-A  The Durbin-Watson statistic
References and selected bibliography
Exercises
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