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90-786
Intermediate Empirical Methods for
Public Policy and Management
Fall 2014
Course meetings:
MW 9:00-10:20AM (HBH 236)
Recitation & Exams:
F 3:00PM - 4:20PM (HBH 1001)
Ben Zamzow, Adjunct Instructor
Email: bzamzow@andrew.cmu.edu
Course Website: http://www.cmu.edu/blackboard
Office Hours Location: TBD
Office Hours: TBD
Pitt Office: 4527 Posvar Hall
Phone: (520) 342-4439
Minor policy changes & adjustments to the schedule or course content may occur at my discretion.
TA: Thomas Goldring
Email: tlpg@cmu.edu
Office: HbH 3045
Office Hours: By appointment.
Phone: TBD
TA: Coco Zhang
Email: xiaokez@andrew.cmu.edu
Office: HBH 245
Office Hours: 2pm-3pm, TR
Phone: TBD
Text(s):
Our primary recommended text is Statistics for Business & Economics, 12th edition, by McClave, Benson, and Sincich. I will supplement the text with some handouts and readings that
will be available on Blackboard.
Software:
Several of the homework assignments will require the use of the statistical software package
Stata. No prior experience with this software is necessary. We will provide instructions on how
to use this program in the Friday review sessions. I will also upload my .do files to Blackboard
for the examples used in class. These will often contain step-by-step comments.
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Stata IC is in the HbH 239 classroom. Stata is also on the Virtual Lab which can be used
by students outside of the classroom:
http://www.heinz.cmu.edu/computing-services/virtual-labs/index.aspx
You may obtain Stata for your personal computer but then will need to pay for a license.
Stata does not offer a site license but we do participate in the GradPlan which gives a discount.
To obtain your own copy you would purchase directly from Stata then access a download copy
of the software. http://www.stata.com/order/new/edu/gradplans/campus-gradplan/
Course Description:
Statistics is the science of summarizing, analyzing, and interpreting data. This class is primarily
intended to provide you with the tools to both comprehend statistical analysis and perform it
yourself. A strong emphasis will be placed on understanding what the specific purpose of each
statistical tool is.
It is important that you leave the class with the ability to do statistical analysis by hand
and on the computer. In class I will typically take a small data set and show you how to do the
statistical calculations by hand. During the review session you will learn how to perform this
analysis on the computer. To be sure that you have gained mastery of both, you will have both
hand calculations and computer problems on your homework.
This course is divided into four distinct parts:
(I) The first part covers descriptive statistics, which involves calculating and interpreting statistical measures to describe raw data.
(II) The second part covers introductory probability theory and key probability distributions.
This will be necessary for understanding the latter material.
(III) The third part will focus on the fundamentals of statistical inference, and will provide you
with the background for executing and interpreting hypothesis tests and confidence intervals.
(IV) The final part of the course covers both simple and multiple regression, one of the most
widely used and powerful statistical tools for policy analysis.
Course Objectives:
The objectives of the course are to provide students with the ability to:
1. Identify and interpret patterns in raw data;
2. Understand basic ideas of probability;
3. Perform and interpret elementary statistical inferences (for example, the capability to compute and interpret hypothesis tests and confidence intervals);
4. Perform and interpret basic regression analyses;
5. Recognize limitations of statistical analysis and identify pitfalls in their interpretation;
6. Use the statistical software package Stata.
Course Grading:
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According to Heinz School policy, the average grade in core courses is expected to be “B+”.
Grades will be approximately normally distributed around that mean. There are two components of your grade. The weighting of these components is:
1. Homework/Cases 20% [8 items, equal weights]
2. Exams/Project 80%
[4 items, equal weights]
• Grades will be maintained in the Blackboard course shell. Students are responsible for tracking their progress by referring to the online gradebook.
• It is your responsibility to make sure that your grades are posted correctly on Blackboard.
Grading or data-entry errors must be brought to my attention within one week of the grade’s
posting. Late grade disputes will not be honored.
Assessment Details:
There will be six homework assignments, two cases, one project, and three exams. Homework
problem sets and cases will be posted online in Blackboard. Each has a specified due date and
must be submitted at the beginning of class that day. Because solutions will be posted immediately after class, you must turn in each of the homework assignments on time or receive a zero.
Homework problem sets will typically require calculations done manually. Each case analysis, on the other hand, will require the use of Stata to employ tools from the course covered by
two homework assignments. Case analysis #1 consists of material from Homework #1 & #2,
Case analysis #2 is based on Homework #3 and #4. In lieu of a third case analysis to cover
material from the final two homework assignments, you will complete a project on a topic of
your choosing.
The research project will provide the opportunity to apply the methods learned in this course to
a policy topic of your own interest. Data access/ability is only constraint as regards appropriate
topics. You will need to develop a policy question that you will be able to address i.e. explain a
causal link between an independent and dependent variable(s) using the data you obtain. You
will present your findings in poster format during the final week of class. Your poster must
contain a concise statement of your research question, your data source, a visual representation of your data, a table containing your results, and a summary interpreting your findings and
acknowledging limitations. A one page research proposal is due in class on November 19th, 2014.
There will also be three exams. Exams will be administered during the Friday review session. Thus, review session is mandatory on the days when exams are given. The first exam
will cover descriptive statistics and probability. The second will cover statistical inference. The
final will be cumulative, but will focus more heavily on material covered after the second exam.
Exams will be in-class and closed book.
I encourage all students to consider that grades are effectively useless to you at this point
in your professional life (other than achieving the threshold level required to assure your graduation). It is my experience that focusing on grades interferes with actual learning. You may
request that the grading of an assignment be reviewed by submitting your original assignment
with a written explanation of why you think the grade was in error.
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Collaboration:
The homework and cases will consist of numerical problems and essay/analysis questions and
are due in class on the date indicated. You are encouraged to work together on these, but each
student must write up their own solutions. If you do decide to work in groups, please include
the names of all collaborators on your assignments. (You will learn more if you ultimately do
your own work.) Plagiarism and/or copying of another student’s work is a university offense.
In general, these rules and the academic integrity standards outlined in your student handbook
will be strictly enforced. Any violation of these rules or standards is considered a fundamental
breach of acceptable professional conduct and will result in failure of the course. No exceptions.
Course Policies:
• As regards teaching and lectures
– My lectures will be based on content from the book, but will not follow along directly.
You should view the text as a complement to the material presented in class.
– The course material will be based on the corresponding chapters in the respective text.
However, we will not be covering every single topic therein. Also, from time to time I
will lecture on material not directly covered by either text, or I may depart from their
treatment of a topic. So come to class and take good notes!
– I view homework assignments as an extension of lectures. Within reason I may sometimes
assign problems not directly drawn from lecture material. However, in all such cases you
will be provided the proper tools to solve these exercises either from the lectures or on
the homework itself.
• General:
– Announcements will be made in class or posted on Blackboard.
– No student may record or tape (video or audio) any classroom activity without my
express written consent.
– It is important to arrive to class on time. To do otherwise creates a distraction for other
students. Arriving to class late on more than one occasion will negatively affect your
grade of the in-class exercises.
– In order to minimize distractions to your classmates we will also have a no electronics
policy: lap-tops, cell phones, and other mobile devices must be turned off during class.
– Do not bring food into the classroom.
Contacting Me:
I spend the majority of my time in my office at Pitt. The best time to meet to discuss the
course is during my office hours as during this time I already plan to be at Heinz. Alternate
meeting times may be arranged on a case-by-case basis as needed.
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Another efficient means to reach me is through email. I will typically respond to emails within
24 hours during the week and within 48 hours on weekends. I have provided my phone number
as a courtesy but I really almost never use the phone.
Disability Statement
If you wish to request an accommodation due to a documented disability, please inform your
instructor and contact Disability Resources: access@andrew.cmu.edu or 412-268-2013 as soon
as possible.
Academic Honesty Policy Summary:
Participants in this course are subject to Carnegie Mellon University’s Policy on Academic
Integrity. Details may be found by accessing the following reference:
http://www.cmu.edu/policies/documents/Academic%20Integrity.htm
Tentative Course Outline:
Readings listed below are complementary to the lectures and should be completed prior to class for
which they are assigned. As I pick and choose topics from each reading to cover in lecture there
is not always a 1:1 correspondence between readings and lecture notes. Please defer to the lecture
notes and problem sets to get an idea of what is likely to be featured on an exam. Links to any
readings other than McClave, Benson, & Sincich will be posted on Blackboard. Even should the
lectures fail to follow the schedule exactly, the assigned readings are still to be completed by the
date listed.
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Class Meeting
Content
8.25 (M)
• Statistics, Data, & Statistical Thinking
8.27 (W)
• Statistics, Data, & Statistical Thinking
8.29 (F)
• Recitation: Statistics, Data, & Statistical Thinking
9.01 (M)
• Labor Day: No class
9.03 (W)
• Methods for Describing Sets of Data
9.05 (F)
• Recitation: Methods for Describing Sets of Data
9.08 (M)
• Methods for Describing Sets of Data
9.10 (W)
• Methods for Describing Sets of Data
9.12 (F)
• Recitation: Methods for Describing Sets of Data
• Problem Set #1 Due
9.15 (M)
• Probability
9.17 (W)
• Probability
9.19 (F)
• Recitation: Probablity
9.22 (M)
• Random Variables and Probability Distributions
9.24 (W)
• Random Variables and Probability Distributions
9.26 (F)
• Recitation: Random Variables and Probability Distributions
• Problem Set #2 Due
9.29 (M)
• Sampling Distributions
• Case Analysis #1 Due
10.01 (W)
• Sampling Distributions
10.03 (F)
• Exam #1
10.06 (M)
• Inferences Based on a Single Sample: Estimation with Confidence Intervals
10.08 (W)
• Inferences Based on a Single Sample: Estimation with Confidence Intervals
10.10 (F)
• Recitation: Inferences Based on a Single Sample: Estimation with Confidence Intervals
• Problem Set # 3 Due
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Class Meeting
Content
10.13 (M)
• Inferences Based on a Single Sample: Tests of Hypotheses
10.15 (W)
• Inferences Based on a Single Sample: Tests of Hypotheses
10.17 (F)
• Mid-Semester Break: No Class
• Recitation: Cancelled..
10.20 (M)
• Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
10.22 (W)
• Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
10.24 (F)
• Recitation: Inferences Based on Two Samples: Confidence Intervals and Tests of
Hypotheses
• Problem Set #4 Due
10.27 (M)
• Inferences Based on Two Samples: Confidence Intervals and Tests of Hypotheses
• Case Analysis #2 Due
10.29 (W)
• Design of Experiments and Analysis of Variance
10.31 (F)
• Exam #2
11.03 (M)
• Design of Experiments and Analysis of Variance
11.05 (W)
• Design of Experiments and Analysis of Variance
11.07 (F)
• Recitation: Design of Experiments and Analysis of Variance
11.10 (M)
• Simple Linear Regression
11.12 (W)
• Simple Linear Regression
11.14 (F)
• Recitation: Simple Linear Regression
• Problem Set #5 Due
11.17 (M)
• Multiple Regression and Model Building
11.19 (W)
• Multiple Regression and Model Building
• Research Proposal Due (1 page)
11.21(F)
• Multiple Regression and Model Building
11.24 (M)
• Multiple Regression and Model Building
11.26 (W)
• Thanksgiving Break: No Class
11.28 (F)
• Thanksgiving Break: No Class
• Recitation: Cancelled..
12.01 (M)
• Multiple Regression and Model Building
• Problem Set #6 Due
12.03 (W)
• Multiple Regression and Model Building
• Research Project Due (Poster format)
12.05 (F)
• Recitation: Multiple Regression and Model Building
12.08 (M)
• (Final Exam) ?
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