


Objectives of the Course
This
course is meant as a review/refresher/reinforcer for those who previously
have been exposed to this material, and is not meant to be an introductory
course in any of these topics. It is
expected that students without previous exposure to these ideas will take
classes from the Department of Mathematics rather than use this class as the
first expose to the material. Overall, this course should
enhance the learning of students in their quantitative courses by providing
them with a recent exposure to the baseline level of mathematical knowledge required in
quantitative courses in the social sciences, and courses in the Department of
Statistics and the CSSS in particular. By ensuring students taking subsequent
quantitative classes have a core set of skills the later classes can focus at
a more sophisticated conceptual level. This course is part
of the curriculum of the new Center
for Statistics and the Social Sciences (CSSS), with funding from the
University Initiatives Fund. The CSSS is includes faculty members from the
Department of Statistics and a broadrange of social science disciplines
including Anthropology, Economics, Geography, Political Science, and
Sociology. This curriculum is been developed to complement and strengthen the
quantitative methods course offerings for social science students at both the
undergraduate and graduate levels. Structure of the CourseThere
will be a once per week integrated lecture on three areas: mathematics,
probability and statistics. About twothirds of the course will be on
mathematics. Course Requirements and GradesThere will be weekly homeworks
and exercises relating to computing and programming. Students will be graded
on a scale of 1 to 10 for each homework. Discussion of homework
problems is encouraged. However, each student is required to prepare
and submit solutions (including computer work) to the assignments and project
on their own; solutions prepared “in committee” are not acceptable.
Duplication of homework solutions and computer output prepared in whole or in
part by someone else is not acceptable and is considered
plagiarism. If you receive assistance from anyone, you must give due
credit in your report. (Example: “Since the data are all positive, and
skewed to the right, a logarithmic transformation is clearly appropriate as a
next step. I thank David Cox for pointing this out to me.”) I welcome comments or suggestions about the course at any time, either in person, by letter, or by anonymous email. Please feel free to use these ways make comments to me about any aspect of the course. Use the menu on the topleft of this page to find out more about the course. STUDENTS WITH DISABILITIES If you have a disability that requires special testing accommodations or other classroom modifications you need to notify the instructor and the Office of Disabled Student Services as soon as possible. You may contact the DSS office at 5438925. 

