NORTHWESTERN UNIVERSITY Department of Statistics. Fall 2012 Statistics 210 Professor Savage INTRODUCTORY STATISTICS FOR THE SOCIAL SCIENCES

Transcription

1 NORTHWESTERN UNIVERSITY Department of Statistics Fall 2012 Statistics 210 Professor Savage INTRODUCTORY STATISTICS FOR THE SOCIAL SCIENCES Instructor: Professor Ian Savage 330 Andersen Hall, , Teaching Assistants: Wendy Chang, Dong Xi, Patrick Zhao, Course Description: The objective of this course is to equip students with the probabilistic and statistical techniques needed to support empirical analysis of data in economics and other social sciences. These analytical techniques are available in commonly used and user-friendly computer programs such as Microsoft Excel and Stata. This course will NOT make extensive use of computer software as the intention is to ensure that the student is familiar with the underlying theory, illustrated by (computationally straightforward) numerical examples. This will ensure that the student will ultimately be an informed user of these programs, and has an understanding of the assumptions and the limitations of various analytical tools. Prerequisites: No formal prerequisites. However, an understanding of basic math concepts at the calculus level is helpful. Course Content: A syllabus of topics to be covered and a class schedule are attached. Course Website: Blackboard will be available for this course. Problem sets and practice examinations will be posted on Blackboard, as will any handouts that are originally distributed in hard copy in class. Scores will also be available through the grade book feature. However, lecture notes and slides will not be posted, and model solutions to the problem sets and examinations will only be available in hard copy at the discussion sections. Lectures: There are three lectures a week on Monday, Wednesday and Friday from 11:00AM to 11:50AM in Harris Hall room 107. There is no class on Friday November 23. Discussion Sections: Students are also required to attend weekly discussion sections held on Tuesday or Thursdays. The same material is presented on both days. Discussion sections will start in the second week of class (October 2 and 4). The discussion section is an integral part of the course, and attendance is expected. It will be used to expand on ideas presented in the readings and in lectures, and to discuss assigned and graded homework problems and examinations. There are no sections on Thursday November 22. Students registered for a specific discussion section in CAESAR. Knowing which students are in which section will make the logistics of returning examinations and problem sets easier. You can swap section using the edit function within CAESAR during the add period

2 Professor Savage 2 Statistics 210 (until Wednesday October 3), provided there is space in the section you wish to move into. Details on each section are: Section # Day Time Location Teaching Assistant 21 Tuesday 4:00-4:50 Swift 107 Patrick Zhao 22 Thursday 4:00-4:50 Tech L150 Patrick Zhao 23/25 Tuesday 4:00-4:50 Francis Searle 2107 Dong Xi 24/26 Thursday 4:00-4:50 Tech L251 Dong Xi Office Hours: Start in week of October 1. Feel free to attend whichever office hours are most convenient to you. TA office hours are held in the lobby of the Statistics Department, 2006 Sheridan Road. Mondays 1:00-2:30 Andersen 330 Professor Savage Tuesdays 10:00-noon 2006 Sheridan Wendy Chan Wednesdays 9:00-10: Sheridan Patrick Zhao 2:30-4:30 Andersen 330 Professor Savage Thursdays 10:00-noon 2006 Sheridan Wendy Chan (11:30-1:30 Andersen 330 Professor Savage on Oct 18 & Nov 15 only) 1:00-3: Sheridan Dong Xi Fridays 9:00-10: Sheridan Dong Xi 1:00-3: Sheridan Patrick Zhao (not Oct 19 or Nov 16) You can arrange for an appointment at other times by ing, telephoning or seeing me or my teaching assistants before or after the lectures and discussion sections. Evaluation: Evaluation will be made on a final examination (40% of the total points), two midterm examinations (25% each), and seven problem sets (10%). P/N is allowed. The midterm examinations will be held during class time on Friday October 19 and Friday November 16, and the final examination from 9AM to 11AM on Friday December 14. No make-ups will be given for either midterm examination. In the event of a genuine, and verified, illness or emergency I will give the other midterm and final examination extra weight in determining the final grade. Permission to take a make-up final examination or to receive an incomplete in the course can only be obtained from the WCAS Office of Studies and not from the instructor. Any student with a documented disability needing accommodations is requested to speak directly to the Office of Services for Students with Disabilities ( ) and the instructor, as early as possible in the quarter (preferably within the first two weeks of class). All discussions will remain confidential. The final exam for the course is scheduled for 9am on the Friday of exam week. This is not great. However, my experience is that changing the time for such a large class is next to impossible to achieve. Also, I strongly believe that all students should be evaluated in a fair and

3 Professor Savage 3 Statistics 210 equal manner. Therefore, I will not agree to an early examination date for any individual student. To do so would require setting a different exam to that for the other students, and I feel that this would not allow fair and equal evaluation. The first midterm examination will cover the material in chapters 1, 2 and of the textbook. The second midterm examination will cover material in chapters , 4, 5 and The final examination is a comprehensive examination covering all material in the course (chapters 1-11 in the textbook). Copies of past examinations (and their solutions) will be posted in the Documents section of Blackboard, and can be used for practice. Text Book: The text for this course is Paul Newbold, William L. Carlson and Betty Thorne, Statistics for Business and Economics (Eighth edition, Prentice Hall, 2013, ISBN ). We will be covering the first 11 chapters. The textbook is available in a number of formats: Full book A cheaper custom edition especially for NU covering just the first 11 chapters which will be available from the Norris bookstore and at Becks An e-book You may find that the book is packaged with access to MyStatLab, an on-line resource of tutorials and practice problems. Students may find this useful, but it is an optional extra (at relatively modest cost) and not required as part of the course. The eighth edition has just been published. Students are welcome to use a used version of an earlier edition, or the custom NU edition covering chapters 1-11 in the Seventh Edition. Problem Sets: There are seven problem sets, which will be graded, and then discussed at the Tuesday and Thursday sessions. Problem Set 1 is attached to this syllabus. Problem Sets 2 through 7 will be posted to the Assignments section of Blackboard. The completed problem sets should be returned to a box marked "ST 210" which will be on your right-hand side in the Economics Department Office (Andersen 302) by 3PM on the Friday prior to the relevance discussion sections (except for problem set 6). Problem sets received late receive zero credit. The schedule for the problem sets is: Set # Hand in by 3 pm on Returned and Discussed 1 5 October 9/11 October 2 12 October 16/18 October 3 26 October 30 October / 1 November 4 2 November 6/8 November 5 9 November 13/15 February 6 Monday 26 November 27/29 November 7 30 November From your TA in Reading Week Advice on Studying: This course is not suitable for pulling all-nighters just prior to examinations. Material later in the course builds on material discussed earlier in the course. It is therefore vital that you keep up with the lectures, readings and problem sets, and ensure that you understand entirely the logic of the models and analytical techniques that are discussed. Therefore if you are confused about any material it is best that you speak to me or one of my

4 Professor Savage 4 Statistics 210 teaching assistants immediately so that we can help you. As this course is quantitative the best studying advice is practice at solving problems. There are problems at the end of each section in the textbook, and with the on-line application sold with the textbook. Rival textbooks, that can be found in the library, also contain problems that you can use for additional practice. Optional Mentoring: The Searle Center for Teaching Excellent offers the Academic Mentoring Program (AMP). This is a peer-led academic support in a small-group setting for students enrolled in this course. If you join the program, you will meet weekly with about 5 8 other students and a peer mentor a student who has already taken and done well in the course. During group sessions, students raise questions they have about the course content and work to develop answers together, with the guidance of the mentor. Past participants report that AMP helped them stay on track with their studying and solidify their understanding of course concepts. AMP is not a drop-in program; students register for the full quarter. Times and locations vary. You may register for AMP at: through Wednesday, October 3rd by 12:00 p.m. *Space is limited* Laptop Computer Policy: Except in the case of proven medical necessity, students may not use a laptop computer or computer tablet during the lectures. Such devices must remain in your bag, and may not be placed on your desk. Mobile Communications Policy: Mobile telephone devices should have the ringer turned off and be placed in pockets or backpacks. Students may not make or receive phone calls, surf the web, or send or receive text messages during class. SYLLABUS Listed below are the topics covered in the course. References to the relevant chapters in the most recent edition of Newbold, Carlson and Thorne are shown in parentheses. At the end is a timetable showing when each subject will be covered. Part 1 Descriptive Statistics The first part of the course equips the students with techniques to describe and illustrate bodies of data. 1.1 Data Representation and Display (Chapter 1) Large quantities of data generally need to be summarized in some way to permit easy interpretation. Tabular forms of data are discussed, and differentiation is made between different types of data: categorical versus numerical, continuous versus discrete. Finally, graphical methods of data display are discussed with an emphasis on the appropriate format to use in different circumstances.

5 Professor Savage 5 Statistics Measures of Location, Dispersion and Shape (Chapter and appendix) Meaningful summaries of large bodies of data can be made by reference to simple measures of the location ( middle ) of the data, how spread out it is, and the shape of the distribution. Various measures will be discussed and their relative merits compared. 1.3 Index Numbers A convenient way of expressing data is by use of an index relating the value of one observation against some base figure. Methods for calculating indices are described and compared. Part 2 Probability Theory Interpreting data and the investigation of hypotheses in the social sciences inherently requires knowledge of probability theory. This is because many of the distributions of data described in part 1 are derived from probabilistic events. 2.1 Introduction to Probability (Chapter ) We formally define what we mean by the probability of an event happening, and describe some typical pitfalls involved in making calculations of probability. 2.2 Marginal, Joint and Conditional Probability (Chapter ) We now considering what happens when two probabilistic events occur at the same time, and discuss how we can tell whether the two events are related to each other. Part 3 Probability Distributions We are now in a position to draw up the distribution of the occurrence of a probabilistic event based on probability theory. The link will be drawn concerning the similarities of the resulting distributions and the kinds of data distributions discussed in part 1 of the course. The great benefit of being able to approximate actual observed data distributions and associated probability-based distributions is that the latter have known properties that can be used for prediction and analysis. 3.1 Random Variables and Discrete Probability Distributions (Chapter ) 3.2 The Binomial Distribution (Chapter 4.4) 3.3 The Poisson Distribution (Chapter 4.5) 3.4 Combinations of Discrete Random Variables (Chapter 4.7)

6 Professor Savage 6 Statistics Continuous Probability Distributions (Chapter ) 3.6 The Normal Distribution (Chapter ) Part 4 Sampling Frequently it is not possible, or even desirable, to collect full sets of data. Therefore, some selectivity, or sampling, procedures are required. This part of the course describes methods of sampling, how to interpret results of samples; and how to use them to test hypotheses from the social sciences. 4.1 Introduction to Sampling (Chapter 6.1) A brief description of why sampling is often necessary, methods of sampling, and the potential pitfalls in selecting a sample. 4.2 Sample Statistics (Chapter ) Summary statistics of the type described in part 1 of the course can be calculated from samples (albeit, that there are subtle differences in formula for some of the measures). Due to the inherent variability in the sampling process, we would not expect to obtain exactly the same sample statistics from each sample. However, statistical theory describes how the calculated sample mean might vary from sample to sample. We describe the resulting sampling distributions and show how they are affected by the size of the sample. 4.3 Confidence Intervals for the Population Mean (Chapters , , 8) Because of the variability in the sampling process, we can only use the calculated sample mean to establish a confidence interval within which the mean value of the population might lie. We discuss how these confidence intervals are calculated, and how they depend on the size of the sample. This permits the discussion of the optimal sample size. 4.4 Confidence Intervals for the Population Variance (Chapters 6.4, 7.5) We will discuss the distribution of variances obtained from samples and the confidence interval for where the variance of a normal distribution might lie. 4.5 Hypothesis Tests (Chapters , , 10.5) Often the purpose of taking samples is to test some hypothesis suggested by social science theory. These tests include deciding whether a particular sample differs from preexisting theories about the mean of the distribution, and deciding whether two separate samples are significantly different. Methods for conducting these tests are

7 Professor Savage 7 Statistics 210 described in detail. We also discuss the possible errors inherent in making these tests, and how these errors can be minimized. Part 5 Correlation and Regression (Chapters 2.4, 11.7, ) We conclude the course with an introduction to methods for determining if two sets of data are related ( correlation techniques ) and how to estimate the value of the relationship ( regression techniques ). This will act as an introduction to detailed consideration of these techniques in courses on regression or econometrics (such as Economics 281 or 381-1). LECTURE SCHEDULE Week 1: Week 2: Week 3: Week 4: Week 5: Week 6: Week 7: Week 8: Week 9: Week 10: Introduction; Data representation and display Measures of location; Measures of dispersion and shape; Index numbers; Introduction to probability Probability theory Random variables and discrete probability distributions; MIDTERM EXAM I Binomial distribution; Poisson distribution; Combinations of random variables Continuous probability distributions, Normal distribution; Introduction to sampling Sample statistics; Confidence Intervals for Means Confidence Intervals for Variances; Hypothesis tests; MIDTERM EXAM II Hypothesis tests (continued) Correlation; Regression