Download PSI TRAINING COURSE

A Two Day Course on Discrete Data Modelling
Presented by
Professor Alan Agresti
6th & 7th October 2016
Venue:
Course
Summary:
Learning
Outcomes:
Topics
Covered:
Target
Audience:
Knowledge
Assumed:
Course
Format:
The Royal Statistical Society, 12 Errol Street, London, EC1Y 8LX
Nearest Tubes: Barbican, Liverpool Street, Moorgate, Old Street
This course provides an applied overview of statistical methods for analysing discrete data, from the
perspective of generalized linear modelling. Each method is illustrated with an example, mainly using R
software but also often showing SAS and Stata code and output. Topics include models for binary
response data, multicategory data, count data, overdispersed and zero-inflated data, and correlated and
clustered discrete response variables.
The main objective of the course is to introduce attendees to the most important statistical methods for
analysing discrete data. Special emphasis will be placed on categorical data, which are very common in
practice, and when there are more than two categories, different methods apply for ordered than for
unordered categories. It is useful for a methodologist to know the various options for analysing such data
as well as the pros and cons of those approaches. The main focus will be on introducing various models
and their interpretations, and showing how to implement them. Through examples, the attendees will
learn how to use the models and will understand the advantages and disadvantages of the various model
types. Although software emphasis will be on R, many of the examples will also show SAS and/or Stata
code and output.
Topics include logistic regression and probit models for binary data, baseline-category logit and
cumulative logit models for multicategory data, Poisson and negative binomial regression models for
count data, zero-inflated models, overdispersion methods including quasi-likelihood and beta-binomial
models, and marginal models and random effects models for correlated categorical response variables.
People with a background in applied statistics, including basic regression and ANOVA. A typical
attendee might be a statistician or biostatistician working in government or industry who never had an
academic course covering the topics treated in this short course.
Basic statistical modelling, such as regression and ANOVA, and confidence interval and significance test
methods used in them that are based on the likelihood function (such as maximum likelihood estimation
and likelihood-ratio tests).
Lecture with worked examples using R software, with many examples also having SAS and/or Stata code
and output, plus two hours for practicals. Begin about 9 am and end about 5:30, but attendees who do
not want to participate in practical sessions can leave earlier.
Registration before
7 September 2016
Registration on / after
7 September 2016
Non Member
£562.50+vat
£625+vat
RSS Fellow
£478.12+vat
£531.25+vat
£450+vat
£500+vat
Fees
(incl VAT)
RSS CStat: also MIS, FIS & GradStat
Contact:
Tessa Pearson, Royal Statistical Society, 12 Errol Street, London EC1Y 8LX.
Tel: +44 (0)20 7614 3947 Email: [email protected] Fax: +44 (0)20 7614 3905
Non-members are welcome to join the Society at the same time as registering for the course and the discount received will cover the
cost of their first subscription payment. More information about membership can be found at www.rss.org.uk/join
REGISTRATION FORM
Discrete Data Modelling – 6 & 7 October 2016
PLEASE USE ONE FORM PER PERSON. Completed forms may be faxed to + 44 (0)20 7614 3905
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COURSE FEES (including VAT @ 20%): Includes full course notes, tea/coffee and lunch
Please tick the appropriate box below:
Registration before
Registration on/after
6 September 2016
6 September 2016
Non-member (£562.50+vat)
£675.00 □
Non-member (£625+vat)
RSS Fellow (£478.12+vat)
£573.75 □
RSS Fellow (£531.25+vat)
£540.00 □
CStat, MIS, FIS and GradStat (£500+vat)
CStat, MIS, FIS and GradStat (£450+vat)
£750.00 □
£637.50
□
£600.00 □
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Please return
completed form to:
Tessa Pearson, Royal Statistical Society, 12 Errol Street, London EC1Y 8LX.
Tel: +44 (0)20 7614 3947 Email: [email protected] Fax: +44 (0)20 7614 3905