Quantitative Methods in Economics, ECON 200 Syllabus Sun-Ki Choi. Section 01, MWF 8:50-10:20 A.M. Section 02, MWF 10:30-12:00 P.M.

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1 Quantitative Methods in Economics, ECON 200 Syllabus Sun-Ki Choi Section 01, MWF 8:50-10:20 A.M. Section 02, MWF 10:30-12:00 P.M. Fall 2016 H 112 Office: Office Hours: Course website: 215A Hepburn Hall TTh 2:00-3:30 P.M. or by appointment schoi@stlawu.edu Sakai Prerequisite: STAT 113 (Applied Statistics). Required Text and Materials Jaggia, Sanjiv and Alison Kelly. Business Statistics: Communicating with Numbers. McGraw-Hill Irwin, Textbook Datasets These datasets will be used for homework and examples that I will work out in class. Course Description Many of the upper division courses in Accounting, Agriculture Economics, Analytics, Economics, Finance, Management, Marketing, and Public Policy use and build upon the statistical techniques and analysis learned in ECON 200. This course provides a survey of empirical techniques relevant to modern economics and business, with a major emphasis on estimation, hypothesis testing, correlation, modeling, analysis of variance, regression, forecasting, and time series analysis. No Pass/Fail grading. Student Learning Outcomes We have the following twelve student learning outcomes for the course: 1. Students will be able to do regression analysis. They will be able to choose a topic conducive to regression analysis, specify a regression equation, enter data into Excel, run descriptive statistics on the data, run regressions, interpret and evaluate the results, and write a report detailing the regression project and the results. This PROJECT will be worth 15% of your grade. 2. Students will be able to evaluate regression results, determining whether the regression coefficients have the expected sign and magnitude, whether the regression coefficients are statistically significant, whether the data set appears adequate, whether the equation 1 P a g e

2 2 P a g e includes irrelevant variables or omits theoretically relevant variables, and whether the goodness of fit of the equation appears adequate. 3. Students will be able to do analysis of variance (ANOVA). They will be able to choose a topic conducive to analysis of variance, arrange data in Excel and run ANOVA, and interpret the ANOVA results. ( Tentative) 4. Students will be able to distinguish within-sample and between-sample variation in ANOVA, and will be able to compare and contrast ANOVA and regression analysis. 5. Students will be able to distinguish between a controlled experiment and an observation study, and explain why regression analysis is needed with an observational study to estimate the impact of one variable on the dependent variable when multiple variables are changing. 6. Students will be able to explain the sampling distribution of an estimator, and the properties of unbiasness and efficiency. 7. Students will be able to use in hypothesis testing either the traditional rejection region approach or the p-value approach. 8. Students will be able to distinguish between quantitative and qualitative variables and will be able to construct and use dummy variables both intercept dummies and slope dummies. 9. Students will be able to explain multicollinearity and its effect on regression results. They will be able to choose independent variables that are not redundant and to run using Excel the correlation matrix, and explain why pairwise correlations do not necessarily detect multicollinearity. 10. Students will be able to estimate using Excel a linear probability model, and be able to interpret the results. (Tentative) 11. Students will be able to explain the four components of a time-series variable, and be able to construct and run a linear trend model and a seasonal dummy variable model. (Tentative) 12. Students will write and communicate orally using statistics to inform conversation. Teaching Philosophy and Methods I believe that working together and teaching others is the best way to learn. During this course you will be expected to work in groups. In each course I teach, I make it a point to refrain from teaching you what to think and try and teach you how to think critically about real world problems. I encourage students to ask questions in class, but I want you to think about your questions before you do. My goal is for you to discover the answer on your own, not for you to use me as a substitute for critical thinking. Our textbook authors explain the statistical concepts in relatively simple terms with an emphasis on business and economic applications. With the in-class assignments, you can get the basics from the text with thoughtful reading.

3 In class, we will work together on the more demanding part of the course analyzing, applying, synthesizing, and evaluating statistical ideas. Regularly, you will work together in class with your classmates. I will ask you to compare answers to a problem or work together on a question with your neighbor or neighbors. Working together in class will increase learning potential and retention. Learning is not a spectator sport: Maximum learning results from maximum involvement. 3 P a g e Task Evaluation Criterion Percentage of Grade Quizzes and Assignments 10% Group Regression Project 15% Exam I 25% Exam II 25% Final Exam 25% Your grade in this course will be based on your performance on three exams (25% each), a group regression project (15%), and quizzes/homework (10%). Final grades will be assigned based on the following scale: Grade Percent Daily Quizzes and Assignments These assignments are not traditional quizzes. Most will be in-class worksheets done as I progress through the lecture or group assignments or problems at the end of class session. I will aim for around 15 of these throughout the term and will drop your lowest 2 scores. These assignments cannot be made up (further emphasizing the importance of class attendance). Exams There are three exams. All exams will be worth 100 points each. The Final Exam is comprehensive, with an emphasis on the material since Exam II. The exams cover material from class, the text, and any additional assigned readings. The course material builds upon itself,

4 so each exam will include concepts from previous exams. If I feel that a curve is necessary, it will be determined separately for each exam. The tentative dates for the exams are (First two exam dates may change, I will discuss in class): Exam I: September 26 Exam II: October 31 Final Exam: Section 01 (MWF 8:50-10:20) December 16, 8:30 A.M.-11:30 A.M. Section 02 (MWF 10:30-12:00) December 15, 1:30 P.M.- 4:30 P.M. Non-programmable calculators can be used for exams. Calculators on your cell phone are not allowed. In each foreseen absence case, written verification will be required at least one week before the scheduled exam. Permission to miss an exam must be secured before the scheduled exam time unless the cause of the absence is unanticipated. If you miss an exam for an unforeseen reason you should contact me as soon as you are physically able to pick up the phone and call me or . The make-up exam will be given soon after the missed exam at a time reasonably convenient to all parties. There will be 1 chance to make up the exam, if you miss the agreed upon make up exam, you get a zero. If you miss an exam and do not receive an excused absence, you receive a zero for that exam. Group Regression Project The projects allow you to do regression analysis instead of just talking about it. The reasons for group projects, as opposed to individual projects, are to share the workload, clarify your understanding of regression analysis through interactions with your group members, and work together as a team to produce a final product. The group projects are worth 100 points. I. Assignment to Groups I will assign you to a group. The group sizes will be 3 to 4 students. Please let me know within the first week if you have a strong personal conflict with anyone in class and would prefer not to work with them. Additionally, I will assign a student to serve as the group leader. (I will only assign someone who is willing to be a group leader.) The group leader s role is to keep the group on task, and to divide the workload among the group members. II. Project Grades The project allows you to perform regression analysis. The reasons for the group projects, as opposed to individual projects, are to share the work load, to clarify your understanding of regression analysis through interactions with your group members, and to work together as a team to produce a final product. I will assign you to a group. The group sizes will be determined later and will depend on how many students are in the class. Please let me know within the first week if you have a strong personal conflict with anyone in class and would prefer not to work with them. Additionally, I will assign a student to serve as the group leader (as long as they are willing to be a group leader). The group leader s role is to keep the group on task, and to divide the work load among the group members. Your grade on the group project will be the simple average of two grades: your group s grade and your individual-contribution grade. Your individual-contribution grade depends on how much you contribute to your group project. To determine your contribution, I will observe you throughout the course, and at the end of the course I will ask each group member to evaluate each group member s contribution. A checklist will be provided soon on what is expected of 4 P a g e

5 each group member. Note that if your individual-contribution grade is below a 75%, then your project grade will consist of only your individual-contribution grade. For example, if your group s grade is 94% and your individual-contribution grade is 70%, then your grade will be 70%, not [(94% + 70%)=2] = 82%. III. Project Parts & Tentative Dates A brief description of each part of the project with the respective tentative deadlines and points are detailed below. i.proposal: 15 points, Due Monday, October 17. Select an interesting problem conducive to regression analysis, appropriate for your assignment and with data available. Explain your project, define the variables, and denote data sources. ii. Data Analysis: 20 points, Due Wednesday, November 2. Specify the regression equation. Identify dependent and independent variables. Enter your data into Excel. Analyze the data to check for mistakes and to get a feel for your data. iii. Regression Results: 30 points, Due Monday, November 28. Estimate regressions. Evaluate and interpret the results, and thoughtfully develop the best regression equation. iv. Regression Revisions, Extensions, Interpretations, & Conclusions: 25 points. Presentations: 10 points, Due Wednesday, December 5. Make needed revisions, consider extensions to the regression equation, interpret your results, summarize your most interesting findings and present your work. A complete description for each project part will be handed out as we progress through the semester. Each part of the project will be written as a paper and each part builds upon the previous parts. During the last two class periods, each group will give about a ten to fifteen minute presentation of their regression results. Being Courteous Be on time and do not leave until class is dismissed. Late arrivals and early departures are disruptive to your fellow students and to me. If you have a long walk to get to class, let me know in advance. If nature calls so loudly that you must answer, please leave and return to the classroom as quietly as possible. Do not carry on private conversations during class. This behavior shows disrespect for your classmates who would like to hear the lecture and it has a negative impact on the learning experience of the entire class. I expect professional behavior. Laptops and Cell Phones Turn your cell phones to vibrate and do not text during class. You can use your laptop to update your notes. You cannot use your laptop for instant messaging, ing, playing games, checking sports scores, and the like. Excused Absences The University defines the following as excused absences: serious illness, illness or death of family member; University-related trips; and major religious holidays. In each case, appropriate 5 P a g e

6 verification may be required. Students missing assignments due to an excused absence bear the responsibility of informing the instructor about their excused absence within one week following the period of the excused absence (except where prior notification is required). Cheating Cheating will not be tolerated, anyone caught cheating will be punished to the full extent outlined in the student handbook. It you are found guilty of academic, I will recommend a minimum punishment of a 0 grade in the course. If you are deemed guilty of plagiarism by purchasing your paper or copying a paper available on-line I will recommend that you be expelled from the university. Don t cheat. Disabilities Statement Students with disabilities are entitled to classroom accommodations as provided by the Disability and Accessibility Services. They can be contacted at or by at disabilityservices@stlawu.edu. Course Outline I: Basic Statistical Ideas 1. Normal Distribution: Ch. 6, Continuous Probability Distributions, Sections 6.2 & 6.3, pp Ch. 7: Sampling and Sampling Distributions, Sections 7.1 and 7.2, pp Ch. 8: Estimation, Sections , pp Ch. 9: Hypothesis Testing Section 9.1, 9.2, and 9.3 II: Regression Analysis 1. Ch. 14: Regression Analysis 2. Ch. 15: Inference with Regression Models 3. Ch. 17: Regression Models with Dummy Variables (Selected sections) 4. Ch. 16: Regression Models with Nonlinear Relationships (Selected sections) III: Analysis of Variance (ANOVA) 1. Ch. 13: Analysis of Variance (Selected sections) IV: Time Series, Forecasting, and Index Numbers 1. Ch. 18: Time Series and Forecasting (Selected sections) 2. Ch. 19: Returns, Index Numbers, and Inflation (Selected sections) *** This syllabus is subject to change (including exam dates). If I do make changes, I will announce them in class and/or class members through Sakai *** 6 P a g e

7 Individual Contribution to Group Regression Project Using the scale below, individually rate each member of your group project, including you. 1 = Strongly Disagree 2 = Disagree 3 = Agree 4 = Strongly Agree Name of Member of Group Project Attended Group Meetings & Responded to s Actively Participated in Group Meetings Flexible for Times to Meet Worked Well with Group Members Willing to Accept Work Completed Assigned Work on Time Proofread & Wrote Portions of Report Enthusiastic & Took Initiative Added Considerable Value to the Group Project Sum of Above 7 P a g e

Office Hours: Mon & Fri 10:00-12:00. Course Description

Office Hours: Mon & Fri 10:00-12:00. Course Description 1 State University of New York at Buffalo INTRODUCTION TO STATISTICS PSC 408 4 credits (3 credits lecture, 1 credit lab) Fall 2016 M/W/F 1:00-1:50 O Brian 112 Lecture Dr. Michelle Benson mbenson2@buffalo.edu