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Motivation and Synopsis
This
course provides an introduction to modern statistical methods for comparing
distributions. Social science
research relies on these methods any time comparisons are made between
groups. When the attribute of interest is continuous, for example racial
differences in life expectancy, or earnings differences between men and
women, the traditional methods make comparisons in terms of means, medians
and standard deviations. Traditional methods, however, provide a weak and
unnecessarily restrictive framework for comparison. Consider the earnings
distribution in the With
the emergence of Exploratory Data Analysis (EDA, Chambers, et al 1983; Tukey 1977) and the
development of high speed computing and graphical user interfaces, there has
been a movement towards more nonparametric
and distribution-oriented analytic
methods. A prominent feature of
these methods is the use of graphical
displays. Graphics exploit
the power of our visual senses to convey information in a direct way. Objectives of the Course
In
this course we will start from scratch and introduce practical nonparametric, distribution-oriented and
graphical analytic methodological
tools to aid social science research. We
will follow the topics of traditional methods courses: univariate and
multivariate summaries; simple and multivariate regression. These will be
supplemented by quantile regression, methods for categorical data and an
overall emphasis on distributional
comparisons. These
methods aim to bridge the gap between exploratory tools and parametric
restrictions. The goal is to present the concepts, theory and practical
aspects of the methods in a coherent fashion, with a minimum of statistical
prerequisites. The
course will have an applied focus on the development of tools for research in
the social sciences. The course will involve the practical application of the
ideas and their implementation through statistical software to make them
accessible to social scientists. 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 broad-range 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 two lectures per week. The lecture on Thursday will sometimes be a
laboratory session. 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 top-left 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 543-8925. |
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