Spring 2006 Course Description
Introduction to Quantitative Analysis
Description: A wise statistician (George E. P. Box) once said, ?To find out what happens to a system when you interfere with it you have to interfere with it (not just passively observe it).? Those of you who are oriented to action will take comfort in these words, and rebel against the notion that merely manipulating a few numbers will tell you everything you need to know about interfering with a complicated environment. George Box was right, and so are you.
On the other hand, interfering with a complex environment is expensive, time-consuming, and fraught with peril. It makes sense to gather information about how the world works, and to predict what will probably happen to it when we enact particular public policies, before we start to screw around with it. It?s also important to be sure you?re solving the right problem in the first place, and to verify that nobody else has already solved the problem for you. This course will help prepare you to make sense of messy problems and decide what to do about them. Over the course of the semester, we will discuss how to:
Frame a complex problem to identify which elements of it are especially critical to making the right decision;
Design a means of collecting more information to resolve the critical uncertainties;
Analyze your data and combine it with the information already at hand; and
Decide what to do, given all the information available.
To accomplish this basic goal, we will consider a wide variety of analytic methods, including Bayesian decision analysis and utility theory, mathematical programming, and statistics up through multiple regression. Students will also learn how to use a variety of computer tools that are widely available in the real world for making decisions. The course grade will be based on problem sets, a term project, and a take-home final exam. Although students with some exposure to MS Excel and statistical methods will find the learning curve shorter and more pleasant, the course is designed for people who have little previous experience with quantitative methods.
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