Teaching
Linear models and analysis of variance
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Particulars:
University College
London, 1st term, 2010-2011, 2011-2012, 2012-2013, 2013-2014, 2nd year
undergraduate.
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Module codes:
STAT2002,
STAT7101
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Description:
The aim of this
course is to provide an introduction to
linear statistical modelling and to the
analysis of variance with emphasis on
ideas, methods, applications and
interpretation of results. On successful
completion of the course, a student will
have an understanding of the basic ideas
underlying multiple regression and the
analysis of variance. Furthermore, the
student will be able to analyse, using a
statistical package, data from some common
experimental layouts, and understand and
know how to check the validity of the
assumptions underlying such analyses.
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Resources:
available at UCL Moodle
(restricted access).
Introduction to practical Statistics
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Particulars:
University College
London, 2nd term, 2010-2011, 1st year
undergraduate.
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Module code:
STAT1006
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Description:
The aim of this
course is to provide an accessible
and application-oriented introduction to basic
ideas in probability and statistics.
On successful
completion of the course, a student should be
able to identify and carry out appropriate
statistical analyses of much-encoutered data settings using a
statistical software package and interpret the resultant output.
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Resources:
available at UCL Moodle
(restricted access).
Advanced topics in Statistics: Asymptotic
Statistics
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Particulars:
University of
Warwick, 1st term, 2009-2010, 4th year
undergraduate and MSc.
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Module code:
ST414
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Description:
The aim of this
topics course is to introduce students to
basic asymptotic methods that are used in
Statistics and to highlight their
importance. Special attention is paid to
likelihood based asymptotics for parametric
models, focusing on the asymptotic
properties of the maximum likelihood
estimator. The course combines the
development of general asymptotic tools
with their application to some much-used
results in statistics (such as, for
example, the derivation of the asymptotic
distribution of the log likelihood-ratio
statistic, the 1/2 adjustment to the
binomial counts for bias reduction in
log-odds estimation etc.). Advantages and
shortcomings of different asymptotic
methods are explored using computer
simulation.
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Resources:
available here
.
