Indicator variables show the presence of a certain non-quantifiable attribute, such as whether an individual is male or female, or whether a house has a swimming pool. 16.10 The Heckit, or Sample Selection Model.14 Time-Varying Volatility and ARCH Models.13.4 Impulse Responses and Variance Decompositions.12.1 AR(1), the First-Order Autoregressive Model.10.1 The Instrumental Variables (IV) Method.9.4 Estimation with Serially Correlated Errors.9.1 An Overview of Time Series Tools in R.8.7 Heteroskedasticity in the Linear Probability Model.8.3 Heteroskedasticity-Consistent Standard Errors.8.1 Spotting Heteroskedasticity in Scatter Plots.7.9.2 ggplot2, An Excellent Data Visualising Tool.7.7 The Difference-in-Differences Estimator.7.4 Indicator Variables in Log-Linear Models.7.3 Comparing Two Regressions: the Chow Test.6.1 Joint Hypotheses and the F-statistic.6 Further Inference in Multiple Regression.5.7 Goodness-of-Fit in Multiple Regression.5.6 Interaction Terms in Linear Regression.5.4 Hypothesis Testing in Multiple Regression.5.3 Interval Estimation in Multiple Regression.5.2 Example: Big Andy’s Hamburger Sales.

4.1 Forecasting (Predicting a Particular Value).

3.7 Testing Linear Combinations of Parameters.

3.4 Confidence Intervals in Repeated Samples.

3.3 Example: Confidence Intervals in the food Model.

3.1 The Estimated Distribution of Regression Coefficients.3 Interval Estimation and Hypothesis Testing.2.8 Using Indicator Variables in a Regression.2.6 Estimated Variances and Covariance of Regression Coefficients.2.5 Repeated Samples to Assess Regression Coefficients.2.4 Prediction with the Linear Regression Model.2.2 Example: Food Expenditure versus Income.

Make a dummy variable stat crunch