1. Bivariate regression: fitting a straight line
Exact versus inexact relationships
The least squares principle
The data
The scatterplot
The slope
The intercept
Prediction
Assessing explanatory power: The R²
R² versus r
2. Bivariate regression: Assumptions and inferences
The regression assumptions
Confidence intervals and significance tests
The one-tailed test
Significance testing: a rule of thumb
Reasons why a parameter estimate may not be significant
The prediction error for Y
Analysis of residuals
The effect of safety enforcement on coal mining fatalities: a bivariate regression example
3. Multiple regression
The general equation
Interpreting the parameter estimates
Confidence intervals and significance tests
The R²
Predicting Y
The possibility of interaction effects
A four-variable model: overcoming specification error
The multicollinearity problem
High multicollinearity: an example
The relative importance of the independent variables
Extending the regression model: dummy variables
Determinants of coal mining fatalities: a multiple regression example
What next?