**Decision Analysis
Health/Public Administration 623**

Spring 1997

Thursdays 6-8:30 (POT 110)

**Instructor:**

Dr. Sheila Murray

431 Patterson Office Tower

Phone: 257-5581

E-mail: semurr1@pop.uky.edu

URL: http://www.uky.edu/Classes/PA623

**Office Hours:**

Tuesday and Thursday 4:30-5:30 p.m.

**Philosophy:**

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:

- You and I share responsibility for the quality of your learning
- The motivation, for both of us, should be the satisfaction that derives from improving the quality of your learning.

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:

- What is the most important thing you learned in today’s class?
- What is the least clear point still remaining at the end of today’s class?

This survey gives me information about what works and provides you with an opportunity to
participate in important course management decisions.

**Course Description:**

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).

**Objectives:**

- Understand the basic concepts of the decision making under uncertainty and resource constraints.
- Understand how to use data to make decisions and ask economic questions.
- Learn by doing

**Skills and Knowledge:**

- Determine optimal strategies when there are several alternatives or there is uncertainty.
- Use linear programming to solve management problems
- Use Excel to solve decision problems
- Understand for each econometric problem the nature of the problem, the consequences for

OLS estimation, how to detect the problem and how to get rid of the problem. - Understand the questions that can be answered with econometrics
- Build econometric models using dummy variables, alternative functional forms and dummy

dependent variables - Use SAS and Excel to estimate regressions
- Write 1-2 page reports that summarize the question, method and results
- Knowledge of sources of data
- Use, manage and document data

**Texts:**

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)

**Course Methodology:**

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 (seebelow) 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.

**Course Evaluation:**

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.

**Manager Reports: **

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.

**Problems:**

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.

**Data Journal: **

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.

**Exams:**

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.

**Late Policy:**

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.

**Scale:**

90-100 ...A

80-89 ...B

70-79 ...C

60-69 ...D

0-59 ...E

**Class Outline and Schedule of Assignments: **

Jan. 16

Introduction and Course Outline

Review of 621 Skills

Readings: ASW: Chapter 1-3, Excel Handout

Jan. 23

Decision Analysis

Readings: ASW: Chapter 4;

Computer Application: Decision Analysis and Spread Sheets, ASW pp. 147-50

Jan. 30

Decision Analysis

Readings: ASW: Chapter 5

Computer Application: Example of Data Collection from ICPSR, Individual Work on Managers
Report 1

Feb. 6

Linear Programming

Readings: ASW Chapters 7 and 8

Computer Application: Spreadsheet Solution of Linear Programs, ASW pp. 335-38

Managers Report 1 due

Feb. 13

Linear Programming

Reading: ASW Chapter 9

Computer Application: Spreadsheet Solution of Linear Programs, ASW pp. 391-393, Work on
Managers Report 2

Feb. 20

Overview of Regression

Reading: S Chapters 1 and 2, Handout on SAS

Computer Application: Using SAS

Managers Report 2 due

Feb. 27

Midterm

Mar. 6

No Class

Mar. 13

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

Mar. 17-21

Spring Break

Mar. 27

Specifying a Regression Model

Reading: S Chapter 6

Computer Application: Obtaining Summary Statistics Using SAS, Predicting Physician Utilization

Apr. 3

Specifying Functional Form

Reading: S Chapter 7

Computer Application: Investigating Wage Differentials

Apr. 10

Forecasting

Reading: ASW Chapter 6, S Chapter 15, Section 1

Computer Application: Work on Managers Report 3

Data Journal Due

Apr. 17

Dummy Dependent Variables

Readings: S Chapter 13

Computer Application: Computing Marginal Effects

Managers Report 3 due

Apr. 24

Efficient Estimation

Readings: S: Chapter 8-10

Computer Application: Testing for Heteroskedasticity and Serial Correlation with SAS

May 1

Review of Regression

Readings: S: Chapter 11

Managers Report 4 due

May 8

Final Exam