1. Log-linear modeling and missing data A short course Frans Willekens Boulder, July 26-30 1999

2. Outline

5. Content of sheets The approach adopted in the course –a probabilistic perspective –a process perspective Data types and observations From observations to variables: the role of uncertainty Uncertainty and risk: risk set and exposure

6. Introduction to probability theory and statistical inference –Observations and random experiments –Random variables and probability distributions Continuous random variables Discrete random variables Plausible observations and plausible models: the maximum likelihood method

7. Analysis of count data: introduction to log- linear models –The Poisson probability model –The log-linear model The log-rate model: statistical analysis of occurrence-exposure rates

8. Logit model, logistic regression, and log-linear model: a comparison –Models of counts: log-linear model –Logit model and logistic regression Data on political attitudes (Payne) Data on leaving home –Construct your own logistic regression model Incomplete data: indirect estimation of migration flows. Summary References: books and web sites