Intermediate Empirical Methods for Public Policy

Intermediate Empirical Methods for Public Policy

and Management

Fall 2000

Course Number:90-786

Instructor:

David R. Merrell

Hamburg Hall 238

268-4070

Office Hours: Tuesday 2:00pm to 5:00pm

Teaching Assistant:

Matt Stanczak

Faculty Assistant:

Gretchen Hunter

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 Participation5%-15%(Default = 10%)

Homework15%-30%(Default = 20%)

Each Exam10%-15%(Default = 10%)

Final Exam30%-40%(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:

DateDayTopicReading Assignment

Aug 28MonMaking Sense of Data:MBC 1; CHS

Data VariationHealth Car Spending (p. 32)

Aug 30WedData Compression for One VariableMBC 2; CHS

Stock Mutual Funds (p. 21-22)

Sep 1FriLab. Meet in HbH A103

2:30pm to 4:00pm

(Note the change from the

standard time!)

Sep 4Mon No Classes—Labor Day Holiday

Sep 6WedData Compression for Two CHS Adoption Rates

(pp. 13-20)

Variables

Sep 8FriLab. Meet in HbH A103

2:30pm to 4:00pm

(Note the change from the

standard time!)

Sep 11MonEthics and the Value of Data

Sep 13WedBasic ProbabilityMBC 3; CHS

Challenger

Sep 15FriLab. Meet in HbH A103

2:30pm to 4:00pm

(Note the change from the

standard time!)

Sep 18MonBayes TheoremMBC 18.9; CHS

Amniocentesis

Sep 20WedRandom Variables and ProbabilityMBC 4.1; CHS

DistributionsDrug Testing

Sep 22FriLab. Meeting in HbH A104.

Sep 25MonDiscrete Probability DistributionsMCB 4

Sep 27WedExam 1. Covers Lectures 1-6.

Sep 29FriLab. Meet in HbH A103

Oct 2MonContinuous Probability Distri-MBC 5; CHS

Butions: The Normal DistributionRacial Imbalance (pp. 72-81.)

Oct 4WedRandom Sampling and SamplingMBC 6

Distributions

Oct 6FriLab. Meet in HbH A103

Oct 9MonThe Central Limit TheoremMBC 6; CHS The

Central Limit Theorem for Census Data

Oct 11WedPoint Estimation:MBC 7; CHS

Reporting of Sexual Partners

Oct 13FriLab. Mettin in HbH A103

Oct 16MonExam 2. Covers Lectures 7-11.

Oct 18WedSample Survey DesignMBC 7.6

Oct 20FriMid-Semester Break

Oct 23MonMid-Semester Break

Oct 25WedModeling and Simulation

Oct 27FriLab. Meet in HbH A103

Oct 30MonPoisson and ExponentialMBC 4.5

Processes

Nov 1WedConfidence IntervalsMBC 7; CHS

Mortgage Rates

Nov 3FriLab. Meet in HbH A103

Nov 6MonHypothesis Testing: ConceptsMBC 8; CHS

Condom Use

Nov 8WedHypothesis Testing: MBC 9; CHS

ApplicationsSubway System

Nov 10FriLab. Meet in HbH A103

Nov 13MonCounts in TablesMBC 17

Nov 15WedExam 3. Covers Lectures 12-17.

Nov 17FriLab. Meet in HbH A103

Nov 20MonLinear Regression and MBC 10; CHS

CorrelationEmergency Calls, Purchasing Power Parity, PCB Contamination

Nov 22WedThanksgiving Holiday

Nov 24FriThanksgiving Holiday

Nov 27MonMultiple RegressionMBC 11; CHS

Electricity Usage, Stock Mutual Funds

Nov 29 WedBuilding a Multiple RegressionMBC 12; CHS

ModelPredicting Adoption Visas, Voting Fraud, Incomes of Long Island

Dec 1FriLab. Meet in HbH A103

Dec 4MonForecasting and Time SeriesMBC 14, 15

Dec 6WedCausality and ExperimentationMBC 16

Dec 8FriLab. Meet in HbH A103

Dec 11MonBayesian StatisticsMBC 18; handout

Dec 13WedReview

Dec ??Final Exam--Comprehensive