Course staffGrading:MIT Department of Urban Studies & Planning 11.220: Quantitative Reasoning and Statistical Methods for Planning I Spring 2005 4-2-6- (G) Lectures: Monday & Wednesday, 11 a.m. – 12:30 p.m., Room 4-231 Recitations: Mondays, 10-11 a.m.; Wednesdays, 2:30-3:30 p.m.; Thursdays, 5:30-6:30 p.m. Computing Labs: Wednesdays, 5-6 p.m.; Thursdays, 5:30-6:30 p.m., Fridays, 9-10 a.m. Course staff Dr. Rhonda Ryznar (Instructor) Prof. Joe Ferreira (Instructor) Office: 9-512 Office: 9-532 E-mail: [email protected] Email: [email protected] hours: W 1:30-3:00, Th 4-5:30 Office hours: M 2:30-4, Th 10:30-12 Kathy Hoag (assistant to Dr. Ryznar) Sue Delany (assistant to Prof. Ferreira) Office: 9-545 Office: 9-530 E-mail: [email protected] Email:[email protected] Jinhua Zhao (TA) Office: 9-569 Email:[email protected] Office hours: Th 12:30-2:30 Shannon McKay (TA) Chris Zegras (TA) Office: 9-569 Office: 10-485 Email:[email protected] Email:[email protected] hours: By appt. Office hours: Th 9:30-11:30 Course description: Many, if not most, planners frequently work with quantitative data. Some summarize, analyze, and present data they have collected themselves or have obtained from secondary sources; others must review quantitative analyses and assess the validity of arguments made therein. This course is designed to prepare you to critically review analyses prepared by others, as well as to conduct basic statistical analysis of data yourself. Using numerous examples of “real world” quantitative analysis related to the planning profession, we will become familiar with a variety of tools for describing and comparing sets of data, as well as those used to generate estimates and test hypotheses. We will also emphasize the development of sound arguments and research design, such that students appreciate both the power and limits of quantitative analysis in argumentation. Unlike many other statistics classes, 11.220 gives particular attention to developing the skill of expressing statistical ideas in clear, simple language. We view these skills as essential for effective planning practice. We will use a variety of software packages in this class, building on students’ experience in 11.204 and demonstrating the application of programs such as Access, Excel, ArcGIS and SPSS to quantitative and statistical analysis. Students are encouraged to begin practicing their computing skills prior to the start of class and to seek the support they need throughout the term. Required textbooks: (Available at the Coop and on reserve in Rotch Library; or, check with MCP2s who may be able to loan/sell their copies to you.) Moore, D. 2000. Statistics: Concepts and Controversies. 5th edition. New York: W.H. Freeman. Meier, K., and J. Brudney. 2001. Applied Statistics for Public Administration. 5th edition. New York: Harcourt Brace. 4 February 2005 14 February 2005 2Recommended textbooks: Davis, J. and R. Ryznar. Lecture Notes for 11.220. Many lecture notes will be available on the web; additional materials will be on reserve in the Rotch Library. Ormsby, et al. 2001. Getting to Know ArcGIS Desktop. Redlands, CA: ESRI Press. ISBN: 1-879102-89-7. (Available at websites such as: gis.esri.com/esripress and www.amazon.com). Articles, book chapters, etc.: (Available in course reader and on reserve in Rotch Library.) Horwitz, L., and L. Ferleger. 1980. Statistics and logic. In Statistics for Social Change. Boston: South End Press. Hodge, G. 1963. The use and mis-use of measurement scales in city planning. Journal of the American Institute of Planners. Lehman, Ann and John Sall. Excerpts from “Why is it Called Regression?” Found at http://www.jmp.com/news/jmpercable/06_summer1998/regression.html. SAS Institute, Inc. Marsh, C. 1979. Opinion polls—social science or political manoeuvre? In Demystifying Social Statistics. J. Irvine, I. Miles, and J. Evans, eds. London: Pluto Press. Monmonier, Mark. 1991. How to Lie with Maps. Chicago: University of Chicago Press. Ormsby, et al. 2001. Getting to Know ArcGIS Desktop. Redlands, CA: ESRI Press. Savas, E. 1973. The political properties of crystalline H2O: planning for snow emergencies in New York. Management Science 20(2). Scanlan, J. 1991. The perils of provocative statistics. Public Interest 120: 3-14. Tufte, E. 1983. The Visual Display of Quantitative Information. Cheshire, CT: Graphics Press. Pages 53-78. Grading: Your grade in this course will be determined by the following formula: Attendance and preparedness (completing reading assignments before class): 5% Homework assignments and quizzes: 45% Mid-term exam: 20% Final paper: 30% Please note that no homework assignments will be accepted past the date that solutions are posted on the class web site (which is generally 48 hours after assignments are handed in). Moreover, any assignment that is submitted past its due date will be penalized one full letter grade for every two days it is late. For example, a homework assignment that reflects “A-” level work but is submitted two days beyond the due date will receive a “B-”. No make-up exams will be given except in extraordinary (i.e., emergency) circumstances. In order to maintain fairness among students and a smoothly running course for all of us, these guidelines will be strictly followed. In determining a final course grade we may also take into account trends in a student’s work and the level of his/her participation in class and recitation, which may also raise a grade at the margin (thus “counting” for somewhat more than 5% of the final grade). Also, be aware that students who have not achieved a sufficient level of command of the material will not receive a passing grade in the course. These students will have to take the course over again, or take an equivalent course in another department or at Harvard.11.220: Quantitative Reasoning and Statistical Methods for Planning I Course schedule Note: Indicates that these sessions will feature computing skills development. Date Series Title Lead Instructor Topic Material Covered Readings Activity Code WeekFeb. 2 (W) Lecture 1 RR & JF Class introduction; Argumentation Logistics, rationale for the course. Building and analyzing arguments. Horwitz & Ferleger (reader); http://www2.sjsu.edu/depts/itl/graphics/main.html; Lecture 1 notes L 1Feb. 2/3/4 (W/Th/F) LAB 1