Stats Central x58805 Multivariate Workshop

Welcome to Stats Central!Stats Central is a statistical consulting unit established at UNSW in 2016 to provide university-wide support for staff and students during study design and analysis.  It is funded through the research division and a growing suite ofpartner schoolsto provide support for UNSW research students and staff in collaborative and consultative roles.  We are currently staffed by six professionally accredited consultants with a breadth of expertise working with researchers across disciplines, and located in the Mark Wainwright Analytical Centre.  Stats Central offers:- Statistical consultations forUNSW staffandstudentsregarding all aspects of the research project (study design, analysis and communication).  Study design advice is free for all UNSW researchers and all consulting services are free for UNSW higher degree research students.  Book a consultationnow.- Intensive short courses on introductory statistics, study design, and regression analysis, at heavily reduced rates for UNSW staff and students.-Grant development and review: Free advice on study design and analysis in the project planning stages, advice on how to write details into your grant application from experienced statisticians, and review of draft applications.  For details write toStats.Central@unsw.edu.au- A hub for biostatisticians and statistical consultants around campus.Is your school or unit interested in partnering with Stats Central?Partner schoolsmake a cash contribution to the scheme in exchange for having a professionally accredited statistician based in their school part-time for enhanced research support.

Stats Central, UNSW Sydney

Multivariate analysis in ecology has been changing rapidly in recent years, with a focus now on formulating a statistical model to capture key properties of the observed data, rather than transformation of data using a dissimilarity-based framework.  In recent years, model-based techniques have been developed for hypothesis testing, identifying indicator species, ordination, clustering, predictive modelling, and use of species traits as predictors to explain interspecific variation in environmental response.  These techniques are more interpretable than alternatives, have better statistical properties, and can be used to address new problems, such as the prediction of a species’ spatial distribution from its traits alone.   This course will provide an introduction to modern multivariate techniques, with a special focus on the analysis of abundance or presence/absence data, starting from a revision of fundamental tools in regression analysis, and extending these techniques to the case where there are multiple response variables.
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Details 
Note:  The course will run on Days 1 to 4 from 9.00 am to 5.00 pm and on Day 5 from 9.00 am to 1.00 pm.
Day 1: Revision of (univariate) regression analysis Revision of key “Stat 101” messages, the linear model, generalised linear model and linear mixed model. R packages: lme4   Day 2: Computer-intensive inference and multiple responses The parametric bootstrap, permutation tests and the bootstrap, model selection, classical multivariate analysis, allometric line fitting. R packages: lme4, mvabund, glmnet, smatr.   Day 3: Multivariate abundance data Key properties, hypothesis testing, indicator species, compositional analysis, non-standard models. R packages: mvabund   Day 4: Explaining cross-species patterns Classifying species based on environmental response, species traits as predictors, studying species interactions. R packages: SpeciesMix, mvabund, lme4.   Day 5: Model-based ordination and inference Latent variable models for ordination, model-based inference for fourth corner models. R packages: boral, mvabund.
Workshop prices: UNSW research student $400, UNSW researcher $700, External $1,000
For further details, email us at Stats.Central@unsw.edu.au

Pioneer International Theatre (Room G04)

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