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. 1 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. 2 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/ 3 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 5 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 6 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. 7