UW-Madison SOC 360 - Statistics for Sociologists I

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Rev 01/18/10 1 Sociology 360, Statistics for Sociologists I Spring 2010 Prof: Christine R. Schwartz 4404 Sewell Social Science 262-5791 [email protected] Office hours: Wed 10:30-11:30am, Thurs 11am-noon or by appointment TA: Bo Hee Min 8144 Sewell Social Science [email protected] Office hours: Mon 11am-1pm, or by appointment Classes: Tuesdays and Thursdays, 9:30-10:45am, 101 Agricultural Engineering Labs: Section 301: Thursdays, 1:20pm-3:15pm, 216 Ingraham (Note: Many of these lab sections will be held in 2470 Social Science) Section 302: Thursdays, 3:30pm-5:25pm, 6101 Sewell Social Science (Note: Many of these lab sections will be held in 3218 Social Science) Course description: This is a first course in statistics designed for students in the social sciences. The first part of the course focuses on descriptive statistics. We will cover strategies for exploring and interpreting data and for examining relationships between variables. Topics covered include: describing data with bar charts, boxplots, and histograms; summary statistics; the normal distribution; scatterplots and correlation; regression; and two-way tables. We will also discuss the strengths and weaknesses of various methods of data production. The second part of the course focuses on statistical inference. In this part of the course, we will discuss the logic and methods of making inferences about populations from sample data. In so doing, you will learn how to test hypotheses with a variety of statistical tests. Topics in this section include: the meaning of statistical significance, how to calculate confidence intervals, and how to conduct statistical tests for means, count data, and regressions. Throughout the course, you will analyze small bodies of data and write up your findings. Prerequisites: Sophomore standing and basic algebra skills. Course website: The course website is available through Learn@UW. If you are enrolled in the course, you can access the site by going to http://learnuw.wisc.edu and entering your NetID and password. Once there, click on the link to Soc 360 under “My Madison Courses.” Course materials: Required Text: Moore, Davis S. 2007. The Basic Practice of Statistics, Fourth Edition. New York: W.H. Freeman. (Available at the University Bookstore.)Rev 01/18/10 2 STATA: We will be using the statistical package STATA for many of the homework problems. Your TA will be providing instruction in STATA in lab. Outside of lab, you can access STATA in the Social Science Microcomputer classroom in 3218 Social Science if there is not another class being held there, or in 4218 Social Science, which is the primary lab for student drop-in use. You will also be able to access STATA remotely from home. Your TA will cover how to do this in lab. Finally, if you anticipate using STATA regularly and would like to purchase it, it is available for a reduced rate through the University. See http://www.ssc.wisc.edu/sscc/info/gradplan.htm for details. Web Resources: Moore’s text comes with many useful online supplements that you are encouraged to explore (http://bcs.whfreeman.com/bps4e). These include self-quizzes, additional exercises, statistical applets, and data sets. In addition, all of the materials available on the free site are on the CD that comes with your textbook. Calculators: You will need a calculator for the homework assignments and exams. It should have a square root and a power function. For inexperienced calculator users, you will find it useful to buy a calculator for which the TA can give you instruction. I recommend the Texas Instruments TI-36X Solar (available at the University Bookstore Digital Outpost), which runs about $20. Homework: There will be homework problems assigned for each lecture, which will usually be due at the beginning of class (9:30am) on Tuesdays. Homework received after 9:30am on Tuesdays will be counted as late. Problem sets will be returned in class on Thursday and discussed in lab. Grading of Homework: Homework will be graded on a “+,” “”, “-“ system. Exemplary assignments will be given “+”s (those that are virtually entirely correct and well-documented), “”s will be given for good assignments, but which have significant deviations from the “+” standard, and “-”s will be given for poor or incomplete assignments. You may also receive a “+” or “-” as an intermediary grade. If you miss a week or turn in an exceptionally poor assignment, you will receive a “0.” Correct answers will be provided and discussed in lab. Penalty for Late Homework Assignments: Homework turned in after the due date but by the beginning of Thursday’s class will receive a maximum score of a “.” If the assignment would have received a “” if turned in on time, it will receive a “-.” Homework will not be accepted after the beginning of class on the Thursday after it was due. You may turn in late assignments to Professor Schwartz’s box on the 4th floor of the Social Science building, to your TA, or in class on Thursday. If an emergency prevents you from turning in your homework on time, you may be able to get a short extension on the homework by contacting the TA before the assignment is due. In addition, since we understand that your schedule may not permit you to devote as much time as you would like to all of the homework assignments, your lowest score will be dropped when computing your final grade.Rev 01/18/10 3 Cooperating on Homework Assignments: You are encouraged to discuss the problems on the weekly homework assignments with other students in the class to further your understanding of the material, but you must write them up independently. Data analysis projects: In addition to the homework, there will be two data analysis projects. These projects are meant to put the material presented by Moore into context and to help solidify what you have learned. The first is a short project that focuses on descriptive statistics and graphical display of data. The final project will require you to apply the skills you have learned throughout the course. Further details will be distributed in class. Exams: There will be three non-cumulative exams. Exam questions will consist of a mix of multiple choice, true/false, and open-ended questions, requiring you to interpret results, discuss appropriate analytic methods, and perform relevant


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