This class will explore how various regression modeling techniques can be used to study practical problems and issues in the social sciences and public policy. The focus of this class is on statistical modeling concepts and how to analyze different problems and data sets. Little attention will be devoted to formal proofs and derivations. The content of this course will be very similar to an introductory Econometrics course, but we will use less mathematics than is typically used in the Economics Department. Course topics will include: A review of basic statistical concepts Dealing with uncertainty in research and policy problems Modeling philosophy (the thought process behind setting up a statistical model) Software options (Excel and SAS will be used) Approaches to estimating relationships (e.g., least squares, likelihood estimation, Bayes law) Interpretation of regression statistics Hypothesis testing (from frequentist and Bayesian perspectives) Multivariate regression Variable selection, specification testing, and functional forms How to identify and address modeling problems (e.g., autocorrelation, multicollinearity, heteroskedasticity, omitted variables bias) Dummy variables Logit, Probit, Tobit, and Poisson models Basic index theory Time series approaches Forecasting techniques Causality A brief introduction to more advanced approaches, including non-parametric regression and simulation techniques. Upon completing this course, students should be able to read, understand, critically interpret, and identify the strengths and limitations of many statistical studies encountered in policy reports and in the literature of the social sciences. The student should also be able competently analyze data sets using common statistical techniques. This class is cross-listed with SDS 385. SDS is the home department.