This section of PA397 will be conducted primarily as a ”web-based” section of the IEM course. This is not a lecture course. It will attempt to follow a “flipped” classroom model in which course content will be delivered online through text readings, supplemental videos, slide presentations, and posted handouts via the Blackboard learning support system. Scheduled meetings of this course (Mondays, 2-5 pm) will focus on student questions about the lesson material, practicing the concepts learned in the online material and collaborative problem solving. These scheduled class meetings will be conducted simultaneously in the classroom and online (via Skype, or another similar communications platform). The course instructor and teaching assistance will also be available to answer questions online during mutually agreed to “office hours” each week. Several graded problem sets, two “take-home” exams, and an oral project presentation will be submitted online.
The course develops basic competence and skills in problem solving and quantitative methods applied to public policy analysis. It emphasizes the art and skill of converting problem descriptions into quantitative models, and the analysis and interpretation of these models.
We will review basic concepts of probability, probability distributions, and descriptive statistics as a means of communicating and describing data of all types. Following a brief discussion of data sources and sampling methods, we study the use of sample data to make estimates of, and inferences about, descriptive parameters of larger populations. Using these inference skills and sample data, we develop and test linear regression models to describe relationships between a characteristic dependent variable (such as income) and one or more other explanatory variables (such as gender, age, etc.).
We then look at how quantitative methods can be applied to support decision-making, using decision matrices and decision trees. With these tools, we evaluate decisions made under conditions of certainty, risk, and uncertainty, We also consider the value of obtaining additional information to support the decision analysis, and how to incorporate the decision maker's value judgments into the decision model. In all our decision analysis work we consider the sensitivity of the decision to changes in the problem conditions. After a brief look at making decisions involving several different, diverse characteristics, (i.e. multiple decision criteria), we introduce the idea of "mathematical optimization" as a way to search for the best possible solution to a decision problem which may have constrained or unconstrained decision parameters. This primarily involves using linear programming modeling and analysis techniques.
In general, the course is an applications-focused course rather than a rigorous theoretical or mathematical development. Emphasis is on the use and interpretation of quantitative modeling and analysis methods in policy evaluation and decision-making. Students are required to make extensive use of Microsoft Excel spreadsheets for homework assignments, applications exercises, and take-home exams. Students also participate in a “phased” semester project on a pseudo-real world problem of their own choosing, making an online oral presentation on the results of their work at the end of the semester. Two “take-home” exams will complement the four or five graded homework problem sets.
Prerequisites:
Successful completion of a "typical" college-level mathematics course, including algebra / linear algebra. Plus, one semester of introductory statistics, or equivalent. Calculus "validation."
Familiarity with computer spreadsheets, or willingness to devote out of class time to become familiar with using spreadsheets such as MS-Excel. "Familiarity" includes recording data in a spreadsheet, performing simple calculations, and using the graphing functions.