PA397 introduces students to fundamental statistical techniques for making and evaluating public policy. The course also provides an opportunity to become proficient in Excel, the most widely used computer software for analyzing quantitative data.
The class will be a mix of:
(a) Conceptual work – understanding the how and why behind statistical methods
(b) Applications – applying the concepts to real data sets to build our analysis skills
Learning Outcomes:
By the end of the semester, students should have a firm understanding of the…
Logic underlying descriptive statistics, basic probability, sampling, inferential statistics, and regression.
Importance of judgment in drawing inferences from data.
"Ins and outs” of Excel.
In addition, students should be able to…
Interpret the results of statistical analyses and models used in public policy research done by others.
Use appropriate statistical analyses and mathematical models in their own work.
Present the results of statistical analyses and models effectively.
Grading:
Grades will be based on a final presentation (35%), mid-term exam (25%), and four problem sets (40%).
The final presentation will be the culmination of a group project where students apply the statistical concepts learned in class to real data sets provided by the instructor. Project expectations and a grading rubric will be provided by mid-semester.
PA397 is a challenging course that moves rapidly. Preparation for each class is essential. Lectures will be most productive if everyone has done the required reading and worked through the relevant examples in the text before class.
The required reading list is relatively short to allow students more time to work on problems and do their own analyses. Students will learn more by puzzling over a problem than by rereading chapters or trying to memorize formulas.
The instructor for this course is an Assistant Professor of Practice. Her teaching style prioritizes hands-on learning and equipping students with key skills most relevant for the policy workplace.