William Berry

• * List of Tables and Figures * Preface for Teachers and Students * Acknowledgments Introduction * The Concept of Causation * Experimental Research * The Logic Underlying Regression Analysis * Some Necessary Math Background The Bivariate Regression Model * The Equation * The Intercept * The Slope Coefficient * The Error or Disturbance Term * Some Necessary Assumptions * Estimating Coefficients with Data from a Sample The Multivariate Regression Model * The Value of Multivariate Analysis * Interpreting the Coefficients of a Multivariate Regression Model * Dichotomous and Categorical Independent Variables * The Assumptions of Multivariate Regression * Choosing the Independent Variables for a Regression Model Evaluating Regression Results * Standardized Coefficients * Strong Relationships Among the Independent Variables: The Problem of Multicollinearity * Measuring the Fit of a Regression Model * Statistical Significance * Cross-Sectional vs. Time-Series Data Some Illustrations of Multiple Regression * Lobbying in Congress * Population Dynamics and Economic Development Advanced Topics * Interaction vs. Nonlinearity * Interactive Models * Nonlinear Models * Dichotomous Dependent Variables: Probit and Logit * Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models Conclusion * Glossary * References * Index

William Berry

• * List of Tables and Figures * Preface for Teachers and Students * Acknowledgments Introduction * The Concept of Causation * Experimental Research * The Logic Underlying Regression Analysis * Some Necessary Math Background The Bivariate Regression Model * The Equation * The Intercept * The Slope Coefficient * The Error or Disturbance Term * Some Necessary Assumptions * Estimating Coefficients with Data from a Sample The Multivariate Regression Model * The Value of Multivariate Analysis * Interpreting the Coefficients of a Multivariate Regression Model * Dichotomous and Categorical Independent Variables * The Assumptions of Multivariate Regression * Choosing the Independent Variables for a Regression Model Evaluating Regression Results * Standardized Coefficients * Strong Relationships Among the Independent Variables: The Problem of Multicollinearity * Measuring the Fit of a Regression Model * Statistical Significance * Cross-Sectional vs. Time-Series Data Some Illustrations of Multiple Regression * Lobbying in Congress * Population Dynamics and Economic Development Advanced Topics * Interaction vs. Nonlinearity * Interactive Models * Nonlinear Models * Dichotomous Dependent Variables: Probit and Logit * Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models Conclusion * Glossary * References * Index

William Berry

• * List of Tables and Figures * Preface for Teachers and Students * Acknowledgments Introduction * The Concept of Causation * Experimental Research * The Logic Underlying Regression Analysis * Some Necessary Math Background The Bivariate Regression Model * The Equation * The Intercept * The Slope Coefficient * The Error or Disturbance Term * Some Necessary Assumptions * Estimating Coefficients with Data from a Sample The Multivariate Regression Model * The Value of Multivariate Analysis * Interpreting the Coefficients of a Multivariate Regression Model * Dichotomous and Categorical Independent Variables * The Assumptions of Multivariate Regression * Choosing the Independent Variables for a Regression Model Evaluating Regression Results * Standardized Coefficients * Strong Relationships Among the Independent Variables: The Problem of Multicollinearity * Measuring the Fit of a Regression Model * Statistical Significance * Cross-Sectional vs. Time-Series Data Some Illustrations of Multiple Regression * Lobbying in Congress * Population Dynamics and Economic Development Advanced Topics * Interaction vs. Nonlinearity * Interactive Models * Nonlinear Models * Dichotomous Dependent Variables: Probit and Logit * Multi-equation Models: Simultaneous Equation Models and Recursive Causal Models Conclusion * Glossary * References * Index