Short Courses
Over the course of the year Stats Central teaches several short courses aimed at researchers across all disciplines. Current offerings include:
 Introduction to R

Introductory Statistics for Researchers

Modelbased multivariate analysis of abundance data using R

Experimental Design

Introduction to Regression Modelling in R
Monday 25th September, 2017
R is widely used and extremely powerful statistical software. This course assumes that you have never used R before. You will learn how to obtain and install R, which is opensource software, and RStudio, which is a versatile, userfriendly interface for using R.
It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers (see below).
This oneday introduction to R will cover some basic features of R and lay the groundwork for you to improve your R skills independently. The course is selfpaced and focussed on developing practical skills.
Introductory Statistics for Researchers
September 2627, 2017
Stats Central and the School of Mathematics and Statistics at UNSW are jointly conducting a short course, Introductory Statisitcs for Researchers, in September 2017. Aimed at research workers, the course provides an overview of statistical design and analysis methods. The course emphasises understanding the concepts underlying statistical procedures (relying on a minimum of mathematics) and interpreting the output from statistical analyses. The statistical package used in the course is R (click here for more information). Please note if you are unfamiliar with R a one day Introduction to R is being run on the 25th September, 2017 (see above).
Introduction to regression modelling in R
June 1921, 2017
The core outcome from this course is to recognise that most statistical methods you use can be understood under a single framework, as special cases of (generalised) linear models. Learning statistical methods in a systematic way, instead of as a "cookbook" of different methods, enables you to take a systematic approach to key steps in analysis (like assumption checking) and to extend your skills to handle more complex situations you might encounter in the future (random factors, multivariate analysis, choosing between a set of competing models).
This threeday short course is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools  you should know about the ttest, linear regression, analysis of variance and know something about orthogonal and nested designs. If you have not used R before, we strongly recommend you attend the Introduction to R course on 13th June. If you need to revise introductory statistics material, you should attend the Introductory Statistics course on 1415^{th} June prior to taking the regression course, which will take such material as assumed knowledge.
Modelbased multivariate analysis of abundance data using R
November 2226, 2016
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 dissimilaritybased framework. In recent years, modelbased 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.
Experimental Design
April 20, 2017
This is a one day short cousre which covers the essentials of Experimental Design. Topics covered include randomisation, controls, sample size, reducing variability and pilot studies. This course will also have a practical component using online tools, excel and G*Power.