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COURSE ANNOUNCEMENT Statistical Models and Methods II
Linda Collins TTH
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| This is the second quarter of a two-quarter
sequence. This description outlines the topics in the
complete two-quarter course. This course presents basic
ideas of probability theory and statistics, and is recommended
for students throughout the natural and social sciences
who want a broad background in statistical methodology
and exposure to probability models and the statistical
concepts underlying the methodology. Probability is developed
for the purpose of modeling outcomes of random phenomena.
Some models are studied mathematically and others via
simulation on a computer. Binomial, Poisson, normal and
other standard probability distributions are considered.
Statistical methods for describing data and making inferences
based on samples from populations are presented. Methods
are illustrated on examples and studied via simulation.
Topics include repeated-sampling frequentist inference,
(consisting of methods for one- and two-sample problems,
analysis of variance, analysis of count data, and correlation
and regression), Bayesian inference, maximum likelihood
estimation, and experimental design. Graphical and numerical
data description are used for exploration, communication
of results, and comparing mathematical consequences of
probability models and data. Mathematics is employed
to the level of univariate calculus but is less demanding
than that required by STAT 24400-24500. Other than the
mathematical level, the content of the two sequences
are similar. Prerequisites: Text
(Required): |