1. Multiple Regression Analysis Bernhard Kittel Center for Social Science Methodology University of Oldenburg

2. The Art of Summarizing Relationships

3. The Straight Line 1998 G. Meixner

4. The Straight Line

5. The Art of Summarizing Relationships

6. Regression Analysis: Issues E(b) =  → Case selection Var(b) → Number of cases Y =  + X  +  (s.e.) Measurement Error Model Specification Ontological Assumptions

7. The Art of Summarizing Relationships  Assumptions  Diagnostics  Residual structures  Modeling Issues  Categorical variables  Time series

8. Day 1 & 2: The Model and its Assumptions  Linearity  Identifiability  Independent variables exogenous  Identically, independently, and normally distributed errors

9. Day 3 & 4: Diagnostics  Do the assumptions hold? –Multicollinearity –Residual analysis Outlying & influential data –Heteroskedasticity

10. Day 5 & 6: Modeling Issues  Beyond linear models? –Functional forms Squares, roots, inverses, logarithms –Categorical factors Dummy variables –Conditional effects Interactive models

11. Day 7: Binary response variables  How should we deal with dichotomous dependent variables? –Probability models: Logit –Maximum likelihood estimation –Interpretation

12. Day 8 & 9 Longitudinal data  How should we deal with repeated observations? –Autocorrelation –Time series analysis –Panel data analysis

13. Day 10: Potentials & Limits of Multiple Regression  Equilibrium analysis  Statistical sophistication vs. measurement precision  Temporality in variables and effects  Levels of aggregation

14. The Art of Summarizing Relationships