Intermediate Empirical Methods for Public Policy
and Management
Fall 2000
Course Number:
90-786
Instructor:
David R. Merrell
dmerrell@andrew.cmu.edu
Hamburg Hall 238
268-4070
Office Hours: Tuesday 2:00pm to 5:00pm
Teaching Assistant:
Matt Stanczak
Faculty Assistant:
Gretchen Hunter
ghunter@andrew.cmu.edu
Hamburg Hall 2102
268-6076
Course Objective:
This course is intended to help students gain a broad understanding of applied
empirical methods for analyzing public policy and management issues. It is
crucial that decision making be based on solid foundations such as statistical
analysis. Hence, decision makers should be well versed in statistical
methodology and at the same time well trained in data analysis. This course will
introduce a number of applied empirical methods ranging from simple hypothesis
testing to regression analysis, from count and duration data distributions to
Bayesian statistics. Since this course is applied in nature, theoretical concepts
from lectures will be coupled with homework and lab exercises that will introduce
students to the practice of empirical methods.
Textbooks:
McClave, James T., Benson, P. George, and Sinchich, Terry (1998) Statistics for
Business and Economics, Seventh Edition, Prentice Hall.
Chatterjee, Samprit, Handcock, Mark S., and Simonoff, Jeffrey S. (1995) A
Casebook for a First Course in Statistics and Data Analysis, John Wiley.
Policy on Collaboration:
Students are encouraged to work together on homework and lab exercises
(especially case studies). Working together can be a very useful tool in gaining a
deeper understanding of empirical methods. However, all students are
responsible for their own work, and students should be clear that examinations
(though open note and open book) will not be collaborative.
Grading:
Students are expected to attend lectures, prepare the assigned readings and
homework exercises, participate in class, and take all examinations. To be sure,
there will be three examinations, one comprehensive final examination, and
weekly homework assignments.
The final grade in the course will be based on 100 total possible points. The final
grade will be determined by a weighting scheme developed by each student
subject to the following scheme:
Class Participation
Homework
Each Exam
Final Exam
5%-15%
15%-30%
10%-15%
30%-40%
(Default = 10%)
(Default = 20%)
(Default = 10%)
(Default = 40%)
This scheme is devised to give students some flexibility in the relative weights
assigned to different portions of the course for which different levels of mastery
are demonstrated. If a student does not specify a formula at the end of the course,
then the default weights will be applied.
Course Schedule:
Date
Day
Topic
Reading Assignment
Aug 28
Mon
Making Sense of Data:
Data Variation
MBC 1; CHS
Health Car Spending
(p. 32)
Aug 30
Wed
Data Compression for One Variable MBC 2; CHS
Stock Mutual Funds
(p. 21-22)
Sep 1
Fri
Lab. Meet in HbH A103
2:30pm to 4:00pm
(Note the change from the
standard time!)
Sep 4
Mon
No Classes—Labor Day Holiday
Sep 6
Wed
Data Compression for Two
CHS Adoption Rates
(pp. 13-20)
Variables
Sep 8
Fri
Lab. Meet in HbH A103
2:30pm to 4:00pm
(Note the change from the
standard time!)
Sep 11
Mon
Ethics and the Value of Data
Sep 13
Wed
Basic Probability
Sep 15
Fri
Lab. Meet in HbH A103
2:30pm to 4:00pm
(Note the change from the
standard time!)
Sep 18
Mon
Bayes Theorem
MBC 18.9; CHS
Amniocentesis
Sep 20
Wed
Random Variables and Probability
Distributions
MBC 4.1; CHS
Drug Testing
Sep 22
Fri
Lab. Meeting in HbH A104.
Sep 25
Mon
Discrete Probability Distributions
Sep 27
Wed
Exam 1. Covers Lectures 1-6.
Sep 29
Fri
Lab. Meet in HbH A103
Oct 2
Mon
Continuous Probability DistriButions: The Normal Distribution
MBC 5; CHS
Racial Imbalance (pp.
72-81.)
Oct 4
Wed
Random Sampling and Sampling
Distributions
MBC 6
Oct 6
Fri
Lab. Meet in HbH A103
Oct 9
Mon
The Central Limit Theorem
MBC 3; CHS
Challenger
MCB 4
MBC 6; CHS The
Central Limit
Theorem for Census
Data
Oct 11
Wed
Point Estimation:
MBC 7; CHS
Reporting of Sexual
Partners
Oct 13
Fri
Lab. Mettin in HbH A103
Oct 16
Mon
Exam 2. Covers Lectures 7-11.
Oct 18
Wed
Sample Survey Design
Oct 20
Fri
Mid-Semester Break
Oct 23
Mon
Mid-Semester Break
Oct 25
Wed
Modeling and Simulation
Oct 27
Fri
Lab. Meet in HbH A103
Oct 30
Mon
Poisson and Exponential
Processes
MBC 4.5
Nov 1
Wed
Confidence Intervals
MBC 7; CHS
Mortgage Rates
Nov 3
Fri
Lab. Meet in HbH A103
Nov 6
Mon
Hypothesis Testing: Concepts
Nov 8
Wed
Hypothesis Testing:
Applications
Nov 10
Fri
Lab. Meet in HbH A103
Nov 13
Mon
Counts in Tables
Nov 15
Wed
Exam 3. Covers Lectures 12-17.
Nov 17
Fri
Lab. Meet in HbH A103
Nov 20
Mon
Linear Regression and
Correlation
MBC 7.6
MBC 8; CHS
Condom Use
MBC 9; CHS
Subway System
MBC 17
MBC 10; CHS
Emergency Calls,
Purchasing Power
Parity, PCB
Contamination
Nov 22
Wed
Thanksgiving Holiday
Nov 24
Fri
Thanksgiving Holiday
Nov 27
Mon
Multiple Regression
MBC 11; CHS
Electricity Usage,
Stock Mutual Funds
Nov 29
Wed
Building a Multiple Regression
Model
MBC 12; CHS
Predicting Adoption
Visas, Voting Fraud,
Incomes of Long
Island
Dec 1
Fri
Lab. Meet in HbH A103
Dec 4
Mon
Forecasting and Time Series
MBC 14, 15
Dec 6
Wed
Causality and Experimentation
MBC 16
Dec 8
Fri
Lab. Meet in HbH A103
Dec 11
Mon
Bayesian Statistics
Dec 13
Wed
Review
Dec ??
Final Exam--Comprehensive
MBC 18; handout