Health/Public Administration 623
Tuesdays 6-8:30 (POT 110)
Dr. Sheila Murray
431 Patterson Office Tower
Tuesday and Thursday 4:30-5:30 p.m.
My goal is to create together with you an environment that encourages autonomy, personal responsibility, continuous learning and the ability to adapt. There are two principles that guide this goal:
In order to focus on the quality of your learning and to increase your active participation in the class, we will assess each class period. In the final minute (or two) of each class, I will ask each of you to respond to scale questions and the following two open-ended questions:
This survey gives me information about what works and provides you with an opportunity to
participate in important course management decisions.
This course presumes that students understand the concepts presented in PA/HA 621, especially probability distributions, expected value, variance, use of Excel and familiarity with SAS. Decision Analysis provides a conceptual understanding of the role that quantitative methods play in the decision making process and the economic way of thinking. The course objective is to acquaint you with the basic concepts of the decision making process (such as decision analysis and linear programming) and econometrics (such as regression). The emphasis is non-mathematical and directed toward learning various techniques and their applications. Thus, the concepts will be introduced through problems, cases and projects that use data and computing software (Excel and SAS).
Skills and Knowledge:
ASW: Anderson, Sweeney and Williams, Quantitative Methods for Business, Sixth Edition (NY: West, 1995)
S: Studenmund, Using Econometrics, Second (or Third) Edition (NY: Harper Collins, 1992)
Class time will be devoted to solving problems. There will be a short lecture on the major points of the topic, but the approach to topics is very applied. We only meet once a week, thus it is imperative that you prepare at least two hours for each hour of class. Class is an opportunity for you to ask questions about concepts, techniques and applications. Exams and assignments will include material that is not explicitly reviewed in class. In order for class time to be productive, each student in the class will be assigned at least one problem that he or she will work out before we review the topic in class. These problems will turned in and I will compile them and distribute in the next class period. The problems will be graded (see
below) and I will call on students to work out the problems in class. We will also meet in the Martin School Computer Lab (Room 402 Patterson Office Tower) in the last half of each class period. In the lab, I will illustrate how to use the statistical software and you will be able to work on problems and projects in class using SAS and Excel.
Your grade will be determined by weighting your performance on two types of writing/analysis assignments (four manager reports and a data journal), the weekly problems, a midterm exam and a final exam.
The reports are case problems that are due throughout the semester. These reports are based on case studies in the text and from handouts that I have prepared. The dates that the reports are assigned are given in the outline that follows. You will have one week to complete the reports, (see late policy). With the exception of Manager Report 4, all data will be given to you. The format of the reports will be detailed on a handout given with each assignment. The first three manager reports each count 7 % of the final grade (21% total). The last managers report is an analysis of an economic question of your choice using data from your data journal. This particular report counts 9% of the final grade.
Each student in the class will be assigned at least one problem that he or she will work out before we review the topic in class. These problems will turned in and I will compile them and distribute in the next class period. The problems will be graded on a check-, check, or check+ basis. No problems will be accepted late, I will drop the three lowest scores. The problems count 5% of the final grade.
The data journal is a log that you will keep throughout the semester. In it you will record your efforts to find, collect, summarize and document data from the SSTARS center, Internet source other than ICPSR and two published sources. The journals will be evaluated twice as listed on the outline. The format of the journal is detailed on a handout. The journal counts 15 percent of your final grade.
There are two exams: a midterm that covers the decision analysis material from the first half of the course and a final exam that covers econometrics material from the second half of the course. Both exams will have a skills section in which you will solve problems similar to the end of the chapter problems and a report section in which you will analyze a problem and write a short version of a managers report. Each exam counts 25 % of your final grade.
Assignments are due at the beginning of the class period. I will accept manager reports and the data journal up to 1 week after they are due, however, a 10 % penalty will be assessed to the grade. Reports more that one week late will not be considered for a grade. Makeup exams will be given only for university defined excused absences.
Class Outline and Schedule of Assignments:
Introduction and Course Outline
Review of 621 Skills
Readings: ASW: Chapter 1-3, Excel Handout
Readings: ASW: Chapter 4;
Computer Application: Decision Analysis and Spread Sheets, ASW pp. 147-50
Readings: ASW: Chapter 5
Computer Application: Example of Data Collection from ICPSR, Individual Work on Managers Report 1
Readings: ASW Chapters 7 and 8
Computer Application: Spreadsheet Solution of Linear Programs, ASW pp. 335-38
Managers Report 1 due
Reading: ASW Chapter 9
Computer Application: Spreadsheet Solution of Linear Programs, ASW pp. 391-393,
Work on Managers Report 2
Overview of Regression
Reading: S Chapters 1 and 2, Handout on SAS
Computer Application: Using SAS
Managers Report 2 due
Learning to Use Regression
Reading: S Chapters 3-5
Computer Application: Estimation and Hypothesis Testing Using SAS
Data Journal 2 Published Sources and Proposals Due
Specifying a Regression Model
Reading: S Chapter 6
Computer Application: Obtaining Summary Statistics Using SAS, Predicting Physician Utilization
Specifying Functional Form
Reading: S Chapter 7
Computer Application: Investigating Wage Differentials
Reading: ASW Chapter 6, S Chapter 15, Section 1
Computer Application: Work on Managers Report 3
Data Journal Due
Dummy Dependent Variables
Readings: S Chapter 13
Computer Application: Computing Marginal Effects
Managers Report 3 due
Readings: S: Chapter 8-10
Computer Application: Testing for Heteroskedasticity and Serial Correlation with SAS
Review of Regression
Readings: S: Chapter 11
Managers Report 4 due