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This course is the first course in applied statistical methods for
social scientists. Students will learn how statistical methods can be
used to conduct causal inferences, exploratory data analysis,
forecasting, and hypothesis testing. The first half of the course will
be devoted to probability theory, which serves as a foundation of
statistical theory. The second half covers the linear model in some
depth and if time permits also introduces generalized linear models.
An emphasis of the course is given to practical data analysis, and
students will learn statistical programming as well as basic
principles of probability theory and statistical inference. This
course assumes the mathematical knowledge taught in POL 502, and
prepares students for the next course in the sequence, POL 572.
Download
the syllabus.
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Probability and Independence
: Probability, Counting Methods, Conditional Probability and Independence.
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Random Variables and
Probability Distributions
: Random Variables and Distribution Functions, Probability Density
and Mass Functions, Random Vector and Joint Distributions.
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Expectation and Functions
of Random Variables
: Expectation and Independence, Moments and Conditional Expectation, Expectation
and Inequalities, Functions of Random Variables.
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Convergence
of Random Variables
: Random Sample and Statistics, Convergence of Random Variables.
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