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ECON 201: Statistical Analysis of Economic and Social Data SDSU, Fall Semester, 2015 Instructor: Ivan Major Professor of Economics, Department of Economics, SDSU e-mail: [email protected] Office: Nasatir Hall #315 Office hours: Mo, We, 9:00–9:50 a.m. Lecture: Mo, We, Fri 11:00–11:50 a.m. Classroom: Hardy Tower #183 Course description This is an introductory course to statistical methods to be used in analyzing social and economic data. You will be acquainted with the principles and methods of how to collect information on your analytic subject and what you need to be careful of. First, you need to learn how to transform your collected information into data that can be analyzed by graphical and quantitative methods. You will also study the ways how the collected information can be organized (grouped) and used for your own research purposes. Next, you learn about graphs and indicators that represent your data in a concise manner. You will be able to assemble graphs and form indicators yourself. You will also learn methods of searching for relationships among different groups of information you had collected. That is, you will become familiar with statistical inference. You will need some basics in probability calculus so that you fully comprehend the usage and message of your analytical tools of statistical inference. By the end of the course you will be able to conduct bivariate regression analysis on your data. The course requires no more than the knowledge of basic algebra. On a few occasions, I shall present the deeper methodological (mathematical) background for those interested, but these presentations will not be part of your exams. Required reading: David S. Moore and William I. Notz, Statistics: Concepts and Controversies, 8th edition, New York, NY: W.H. Freeman and Co, 2013. (Referred to as M-N in the syllabus) The book is already available at the SDSU bookstore. The course has a Web site: http://blackboard.sdsu.edu and look for ECON 201 under my name where you will find the syllabus and the lecture notes. Course objectives This course is about analytical tools rather than economic or social substance. But you will not be meeting sterile mathematical concepts and formulas. My purpose with this course is as follows: 1 1. By the end of this course you will be able to reason and make your own judgment with the help of data. 2. You learn to master the tools of elementary statistics, or the methods you can use to find the underlying patterns in, and the social or political implications of, your data. 3. You acquire basic computer skills so that you can handle and analyze large amounts of information. Grading There will be five exams, all closed book and notes. If you complete your five exams successfully, that will set your final grade. There will not be make-up exams. If you miss two or more exams you must take a comprehensive final exam on all of the course material. 5 in-class closed book exams max 20% each Final grades for this course will be calculated as follows: Grading 98–100% 94–97% 90–93% 85–89% 80–84% 75–79% 70–74% 65–69% 60–64% 50–59% <50% A+ A AB+ B BC+ C CD F Course schedule Week 1 Introduction: Where Do Data Come From? Social Research and Statistics Brief Introduction to SPSS 2 Readings: M-N, pp. XIX–XXV. Week 2 Producing Data: Sampling Samples, Good and Bad What Do Samples Tell Us? Sample Surveys in the Real World Reading: M-N, Chapters 1–4 Week 3 Experiments and Data Ethics Reading: M-N, Chapters 5–7 1st exam on Friday of Week 3: Collecting and understanding data; Sampling; Experiments Week 4 Measurement and Graphs Reading: M-N, Chapters 8–11 Week 5 Measures of Central Tendency and Variability Reading: M-N, Chapter 12 Week 6 Measures of Association for Nominal and Ordinal Variables Reading: M-N, Chapters 16. 2nd Exam on Friday of Week 6: Principles of measurement; Graphs; Measures of CT and VAR; Measures of Association Week 7 Probability and statistical analysis 3 Reading: M-N, Chapters 17–20. Week 8 Normal Distribution and its applications Reading: M-N, Chapter 13 Week 9 Scatterplots; Correlation 3rd Exam on Friday of Week 9: Probability; Scatterplots; Correlation; Normal distribution and its applications Reading: M-N, Chapter 14 Week 10 Regression and Inference Reading: M-N, Chapter 15 Week 11 Estimation Reading: M-N, Chapter 21 Week 12 Inferential Statistics 1 Reading: M-N, Chapters 15, 21 4th Exam on Friday of Week 12: Regression; Estimation; Inferential Statistics Week 13 Inferential Statistics 2 Reading: M-N, Chapter 23 Week 14 Testing; Different Types of Tests 4 Reading: M-N, Chapters 22, 24 Week 15 Final overview of what you have learnt in this class. 5th exam on Friday of Week 15: Testing and Different Types of Tests. 5