This is a class on regression analysis. Regression analysis is a specific set of statistical techniques that are widely used in the social sciences today. For example, regression techniques are regularly used in Public Administration, Public Policy, Economics (both macro and micro), Political Science, and Psychology. Regression techniques are also widely used in government and consultant analysis, especially at the Federal level. Further, one of the goals of the MPA program, and a skill that is sought by some employers of our graduates, is to help you become comfortable with the use of numeric information and the ability to translate that information into policy meaning. This class will help you practice that broad skill.
Though our book is called Econometrics, the tools and techniques discussed are widely applicable to understanding causal relationships beyond economic ones and, therefore, making decisions about how to act. Even if you think you wonÕt use regression yourself there may be many instances where someone else will present regression results to you, or where youÕd like to read an article using such techniques. This class will give you a basic understanding of regression analysis and how to look at results and interpret what they mean. I firmly believe this is likely to be useful in your futures. For example, would you like to know what factors cause clients to be more or less satisfied with your agencyÕs services? Regression can help you answer that question.
For me, one of the most exciting things I learned in graduate school was regression analysis. Regression analysis is the Òvirtual laboratoryÓ for public administrators and policy analysts, for only rarely can we actually do experiments, and even true randomization does not provide full control. Even when doing experiments, it is often beneficial to use regression techniques statistically to control for important causal factors that random assignment did not control for. For me, this is wonderful, useful stuff, and I hope I can make you feel that way, too.
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