Empirical Analysis I (4 credits): Fall 2015
PA 5031
Lecture 8: 11:15 ~ 12:30 Tuesday & Thursday Blegan 155
Lab 10: 2:30 ~ 3:45 Tuesday HHH 85
Lab 9: 1:00 ~ 2:15 Thursday HHH 85
Instructor & Teaching Assistants
Jason Cao, 295G Humphrey School, cao@umn.edu, 625-5671.
Office hour: 1:15-2:15 Tuesday and by appointment
Besides office hour, the best way to reach me is by email. Start with PA5031 and several-word summary
of your questions in the subject. I may copy individual questions to all students in the class if they are
common questions.
Greg Lewin (lewin040@umn.edu) and Man Xu (xuxx0460@umn.edu)
See Moodle site for TAs’ office hour
Course Objectives
The objectives of this course are to help you
 evaluate empirical evidence in the media and scientific articles;
 comprehend principles of probability theory and statistics, apply them in research or policy
analysis, and infer policy implications from statistical analysis;
 understand basics of sampling and survey administration; and
 establish a foundation for advanced statistics and survey design.
In this section, we will use examples in urban and regional planning, as well as public policy. We will use
Stata to analyze data on land use, transportation, and health in the lab.
Textbook
Freedman, David, Robert Pisani, and Roger Purves (2007). Statistics, 4th edition. New York: Norton.
ISBN 0-393-92972-8. (Most lectures are adapted from this book. So are most homework and exams.)
Utts, Jessica (2005). Seeing through statistics, 3rd edition. Belmont, CA: Thomson Brooks/Cole. (We
will cover a few chapters of this book. It is not required to buy.)
Both books will be on reserve in the Wilson Library. Other readings will be posted on the course web.
Teaching Styles
Research shows that students learn more and remember what they learn much longer when they are active
participants in the learning process. Be ready to participate in group discussions, think-pair-share, inclass exercises, and so on. The goal of these strategies is to facilitate your learning through engagement.
Questions in Class
I strongly encourage you to ask questions. Framing questions is part of the learning process. Some
questions I will answer right away, because it is important to clear up a confusing point that is critical to
our topic. Some questions are ones to which I will be unable to give a clear answer immediately, without
creating more confusion. I will think about those questions and answer in the next class. Thoughtful
students also come up with a wide range of questions that are beyond what we are in class. You are
welcome to ask such questions, but I may postpone the answer to later in the course or ask you to save the
question for a more advanced course. This has nothing to do with your intelligence or ability to grasp
concepts; rather, it has to do with the sequential nature of statistical learning.
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Expectations
This class is demanding. It covers a lot of material at a pace that students describe as “relentless” or
“frantic” or (more positively) “high energy.” It requires considerable effort and outside-of-class time.
UM policy states that for each credit hour of a class, undergraduates are expected to work three hours –
counting class time, lab time, and study time – to achieve an average grade. If we apply that policy to this
graduate-level class, that means a work load of 12 hours per week for this class, implying at least 8 hours
per week outside of class/lab. Count on it.
(see http://policy.umn.edu/Policies/Education/Education/STUDENTWORK.html)
The goal of the instructor and TAs is for every single student to succeed in this class. We expect you to
work very hard on your own, with each other and with us, to accomplish this goal.
Grading
15+15%
20+20%
10%
10%
10%
Homework (five from lecture, five from lab)
Exams 1 & 2
Lab quiz
Multivariate analysis exam
Lab participation
The homework is a deliberately sizable portion of your grade, as (1) it is in your best interests to do it and
keep up, and (2) it helps take some of the stress off the exams, and can help bring up your final grade if you
have difficulty with the time pressure of exams. Each question of textbook homework is worth 6 points, or
0.3 points of the final grade. Lab homework will be group-based. You are expected to work cooperatively
in groups assigned by TAs. All group members are responsible for the quality of the homework. Only one
grade will be given to each group. If your group is not working well for you, please talk to me or TAs as
soon as possible. Personalities or schedules occasionally cause conflict that is no one’s fault. For all
assignments, the penalty for each day of delay (1 minute to 24 hours) is worth 20% of the assignment
grade. Lab quiz questions will be distributed at least one week before the quiz. You will not have access
to quiz data until the quiz takes place.
A significant proportion of questions in exams will be adapted from textbook homework, textbook
examples, and other review exercises. It is of your interest to work on those questions. The key to a
decent grade is to show your work, not only the answers. Exams 1 and 2 will be tested twice.
Specifically, each student will take the exams individually for 75 minutes on the exam day; then the group
will be tested using the same questions for 25 minutes in the following lecture. The score of the individual
exam accounts for 60% of your grade and the score of the group exam accounts for 40%. Open-book
multivariate analysis exam questions will be distributed by email at 11:15 am on the examination day.
Make sure to check your umn email. If you have any concern regarding exams, come to me before exams.
No excuse will be accepted after the exams. Doctor’s note is required for make-up of the exams. There is
no final exam.
To avoid free-ride, your group members will evaluate your participation in group discussion and
assignments. The grade of group participation will be based on two confidential group evaluations (one
at the midterm and the other at the final).
Web Sites
Some class materials will be available on class website. To access the class website:
1. Go to http://www.myu.umn.edu.
2. Log in with your University of Minnesota Internet ID (X.500 username) and password.
3. Find the Moodle site link for PA 5031.
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Stata Tutorials
Princeton add UCLA provide free tutorials via the internet
http://www.ats.ucla.edu/stat/stata/sk/default.htm
http://data.princeton.edu/stata
Course Policies
Academic Dishonesty: Students are expected to do their own assigned work. If it is determined that a
student has engaged in any form of Academic Dishonesty, he or she may be given an "F" or an "N" for
the course, and may face additional sanctions from the University. Academic dishonesty in any portion
of the academic work for a course shall be grounds for awarding a grade of F or N for the entire
course. See http://www1.umn.edu/regents/policies/academic/Student_Conduct_Code.html.
Diversity and Collegiality: This course draws graduate students from a variety of disciplines. This
diversity of academic experience, assumptions regarding learning, and ways of approaching problems
is one of the most enriching aspects of the course. In addition, every class is influenced by the fact that
students come from widely diverse ethnic and cultural backgrounds and hold different values. Because
a key to optimal learning and successful teaching is to hear, analyze, and draw from a diversity of
views, the instructors expect collegial and respectful dialogue across disciplinary, cultural, and
personal boundaries.
Student Conduct: Instructors are responsible for maintaining order and a positive learning
environment in the classroom. Students whose behavior is disruptive either to the instructor or to other
students will be asked to leave. Students whose behavior suggests the need for counseling or other
assistance may be referred to their college office or University Counseling and Consulting Services.
Students whose behavior may violate the University Student Conduct Code may be referred to the
Office of Student Judicial Affairs.
Sexual Harassment: University policy prohibits sexual harassment as defined in the University
Policy
Statement
(http://www1.umn.edu/regents/policies/humanresources/SexHarassment.html)
adopted on December 11, 1998. Complaints about sexual harassment should be reported to the
University Office of Equal Opportunity, 419 Morrill.
Accommodations for Students with Disabilities: Participants with special needs are strongly
encouraged to talk to the instructors as soon as possible to gain maximum access to course
information. All discussions will remain confidential. University policy is to provide, on a flexible
and individualized basis, reasonable accommodations to students who have documented disability
conditions (e.g., physical, learning, psychiatric, vision, hearing, or systemic) that may affect their
ability to participate in course activities or to meet course requirements. Students with disabilities are
encouraged to contact Disability Services and their instructors to discuss their individual needs for
accommodations. Disability Services is located in Suite180 McNamara Alumni Center, 200 Oak Street.
Staff can be reached at http://ds.umn.edu or by calling 612/626-1333 (voice or TTY).
Student Mental Health: As a student you may experience a range of issues that can cause barriers to
learning, such as strained relationships, increased anxiety, alcohol/drug problems, feeling down,
difficulty concentrating and/or lack of motivation. These mental health concerns or stressful events
may lead to diminished academic performance or reduce a student's ability to participate in daily
activities. University of Minnesota services are available to assist you with addressing these and other
concerns you may be experiencing. You can learn more about the broad range of confidential mental
health services available on campus via http://www.mentalhealth.umn.edu/
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Acknowledgements
Some sections/sentences were adapted from the syllabus of Dr. Mokhtarian of UCDavis and of Dr.
Levison of the Humphrey School.
Lecture and Lab Schedule
WEEK 1
September 8: Introduction
Introduction to course and LU-T data
September 10: Histograms
Read Freedman et al, 3.1-3.3
Lab 1
 Introduction to land use-transportation data
 Introduction to STATA
WEEK 2
September 15: Types of variables, average, and standard deviation
Read hand-out on scale of variables
Read Freedman et al, 3.4, 3.9; Chapter 4
September 17: Uses of the normal curve
Read Freedman et al, Chapter 5
Lab 2
 Introduction to STATA
 Histograms, mean, median, mode, range, and standard deviation
WEEK 3
September 22: Percentiles and inequality
Read Freedman et al, Chapter 5
September 24: Measurement error, scatter diagrams and correlation coefficient
Read Freedman et al, Chapters 6, 7, 8.1, 8.2, 8.4, 8.6, and 9.1
Lab 3
 Review exercises: Chapters 3, 4, and 5
WEEK 4
September 29: Simple regression
Read Freedman et al, Chapter 12.1
October 1: OLS and the r.m.s. error for regression
Read Freedman et al, Chapter 11.1, 11.2
Read hand-out on OLS (equations are optional)
Lab 4
 Review exercises: Chapters 6, 8, 9, 11, and 12
WEEK 5
October 6: Regression diagnostics
Read Freedman et al, Chapter 11.3-11.5
Read handout on diagnostics
October 8: Experiments and observational studies
Read Utts Chapter 5.1, 5.2, 5.4
Read Freedman et al, Chapters 1.1, 1.2, 1.4 and 2
4
Lab 5
 Scatter plots and correlation coefficients
WEEK 6
October 13: Probability
Read Dr. Levison’s summary
Read Freedman et al, Chapter 13 (not including 13.5) and 14
October 15: Binomial Formula, Law of averages, box models, expected value, standard error,
Read Freedman et al, Chapters 15, 16, and 17
Lab 6
 Linear regression and diagnostics
WEEK 7
October 20: Central Limit Theorem, use normal curve
Read Freedman et al, Chapters 17 and 18
October 22: Exam 1 (covering Weeks 1-5)
Labs 7 & 8
 Review exercises: Chapters 13-18
WEEK 8
October 27: sample surveys and survey methods and chance errors in sampling
Read Freedman et al, Chapters 19 and 20
Read Utts, 4.2, 4.4-4.6
October 29: Accuracy of percentages, confidence intervals and accuracy of sample averages,
Read Freedman et al, Chapters 21 and 23
Read web links on margin of error
Labs 7 & 8
 Review exercises: Chapters 13-18
WEEK 9
November 3: Current Population Survey and how to conduct a poll
Read web links and Freedman et al, Chapter 22 (pp. 395--405, 407--408).
November 5: Null & alternative hypotheses, Z- and t-tests of significance
Read Freedman et al, Chapter 26
Lab 9
 Review exercises: Chapters 20-23
WEEK 10
November 10: Significance tests for differences in averages
Read Freedman et al, 27.1, 27.2, 27.5, 27.7
November 12: Chi-square test
Read Freedman et al, 28.1, 28.2, 28.4-28.6
Lab 10
 One sample test
 Independent sample test
 Paired sample test
WEEK 11
November 17: Multivariate OLS regression
Read hand-out: Ritter, Joseph (2010) “Introduction to Multivariate Regression'' Sections 1-3
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November 19: Multivariate OLS
Read Ritter (2010) Section 4
Lab 11
 Chi-square test
 Review exercises: Chapter 26-28
WEEK 12,
November 24: Exam 2 (covering Week 6-10)
November 26: Thanksgiving
 No lab
WEEK 13
December 1: Multivariate OLS
Multicollinearity
Read Ritter (2010) Sections 4 and 5
Hersch and Straton (1995)
December 3: Multivariate OLS
Read Ritter (2010) Sections 6, 7 and 11
Lab 13
 Multiple regression
WEEK 14
December 8: What educated citizens should know about statistics and probability
Read Utts (2003) and Ziliak and McCloskey (2004)
Read Freedman et al, Chapter 29
December 10: Multivariate analysis mock exam
Lab 14
 Lab quiz
WEEK 15
December 15: Multivariate analysis exam

No lab
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Important Dates:
Solutions to lecture homework are available on the Moodle site.
Items
Lab Assignment 1
Homework 1
Homework 2*
Lab Assignment 2
Exam 1
Lab Assignment 3
Homework 3
Homework 4*
Lab Assignment 4
Exam 2
Homework 5 *+
Lab Assignment 5
Lab Quiz
Open-book exam
Content
Check course website
Chapter 3: 8.2, 8.4 on pp. 50-52
Chapter 4: 8.1, 8.6, 8.7, 8.9 on pp. 74-75
Chapter 5: 7.1, 7.7, 7.9 on pp. 93-95
Chapter 6: 5.2, 5.4 on pp. 104
Chapter 8: 5.1, 5.7, 5.9 on pp. 134-137
Chapter 9: Exercise Set A 6 on p. 143
Chapter 11: 6.4, 6.5 on pp. 198-199
Chapter 12: 4.1, 4.3, 4.7, 4.8 on pp. 213-215
Check course website
Check course website
Chapter 2: 6.1, 6.4, 6.10 on pp. 24-27
Chapter 13: 6.4, 6.9 on p. 235
Chapter 14: 6.5, 6.7, 6.9 on p. 253
Chapter 15: 3.3, 3.8 on pp. 261-262
Chapter 16: 5.4, 5.7 on p. 285-286
Chapter 17: 6.1, 6.2 on p. 304
Chapter 18: 7.2, 7.11 on p. 329
Chapter 20: 6.3, 6.4, 6.7 on pp. 371-372
Chapter 21: 6.5 on pp. 392
Chapter 23: 5.3, 5.4, 5.10 on p. 426-427
Check course website
Chapter 26: 7.2, 7.5 on pp. 495-497
Chapter 27: 6.5, 6.7 on pp. 518-520
Chapter 28; 5.2, 5.9 on pp. 541-543
Check course website
Multivariate regression
Due Day
Sept. 22/24 in lab
Sept. 29 5 pm
Oct. 6 5 pm
Oct. 13 in lab
Oct. 22 in class
Oct. 27/29 in lab
Nov. 3 5 pm
Nov. 10 5 pm
Nov. 17/19 in lab
Nov. 24 in class
Nov. 24 5 pm
Dec. 1 in lab
Dec. 8/10 in lab
11:15-1:00 on Dec. 15 by email
to Cao
* You are not able to get feedbacks from TAs before exams. Please check the solutions of these questions
to make sure you understand how to address them. If you have questions, please visit me or TAs during
office hour.
+
when answering these questions, please follow the steps we discussed in the class.
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