This is a class in applied single equation regression analysis. It will be assumed that students have mastered the topics of basic statistics. Based on this assumption, we will examine regression analysis and its related techniques of quantitative analysis in two steps. The first topic is a review of the single equation OLS regression model in bivariate and multivariate applications. The emphasis would be developing an understanding of the logic of OLS regression, understanding of the parameter estimates they generate, and understanding the use of statistical tests to assess such models. The second topic will be a review of the assumptions underlying the OLS regression model. Each assumption – concerning specification, homoskedasticity, collinearity, and so on – will be identified and their implications for evaluating the OLS regression results discussed. We will then consider diagnostic tools to assess the presence and severity of violations of the OLS assumptions and – where possible – consider how they might be corrected. The emphasis throughout will be on the substantive interpretation of regression results.
Each class will have a lecture on the day’s topic. In contrast, much of the time in the workgroups will be devoted to discussion of empirical results students will generate from a common data set provided to all on the topic of the lecture from the prior class. We will try to develop a collective interpretation of these results. This will require, of course, that students stay current with course assignments
To be announced
To be announced
Teusday 2/11-14/12 van 9-11 in SA-41
Tuesday 2/11-14/12 van 13-14 in SA-41
Let op! Er kunnen nog wijzigingen optreden in dit rooster
Laatste wijziging: November 2010