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
  • Model-based multivariate analysis of abundance data using R
  • Experimental Design
  • Introduction to Regression Modelling in R
  • Advanced Statistical Methods in Epidemiology


Advanced Statistical Methods in Epidemiology

6-7 November, 2017

This workshop is aimed at health researchers and practitioners who are familiar with basic statistical methods used in epidemiology and who would like to expand their knowledge and skills in this area.

The workshop will use one or more existing epidemiological data sets to illustrate how to use software to conduct statistical analyses and interpret statistical results.

Course Outline

  • Introduction to statistical methods for case-control studies (Note 1)
  • Measures of comparative risk (odds ratio, relative risk) and study design (case-control, RCT)
  • Combining comparative risk measures, confounding and interactions
  • Logistic, Poisson, multinomial and conditional logistic regression
  • Introduction to key elements of systematic reviews for meta-analyses (Note 2)
  • Simple meta-analysis model including assumptions
  • Statistical software for meta-anlayses
  • Heterogeneity (random vs fixed effects meta-analysis, tests for heterogeneity, index of heterogeneity)
  • Assessing publication bias (funnel plot, regression test, rank correlation test)
  • Generating forest plots
  • Meta-regression for study-level predictors
  • Multivariate meta-regression for multiple study-level outcomes
  • Kaplan-Meier Survival Estimator
  • Cox Proportional Hazards Models

Note 1: In contrast to clinical trials, case-control studies are observational in nature. In an observational study, the role of the investigator is passive: they observe the individuals and collect relevant data, but do not influence the course of events.

Note 2: It is assumed participants are familiar with the basic conduct of systematic reviews but not yet experienced with conducting meta-analyses.

Software used in the course: R, RStudio, metafor package for R.


Duration: 2 days, 9.00 am to 5.00 pm daily

Dates: 6-7 November, 2017


UNSW Students: $200

UNSW Staff: $400

External (no UNSW affiliation): $1,000


Additional notes

Participants will be given workshop slides, R code and data shortly before the course.

Please bring your own laptop computer to the course.

Course fees include morning and afternoon teas and lunch on both days.


Course presenters

Assoc. Professor Jake Olivier, School of Mathematics and Statistics & Stats Central, UNSW

Dr Zhixin Liu, Stats Central, UNSW






Introductory Statistics for Researchers

26-27 September, 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 25 September, 2017 (see above).
Introductory Statistics for Researchers - September 2017
In many disciplines, researchers wishing to publish are asked to provide a rigorous statistical analysis. Reviewers are often specific about what statistical measures they want included. "Why wasn't Fisher's Exact Test used?" "Was an appropriate sample size determined a priori?"
Statistical analyses require specialised software to perform calculations. In this course, we use the free statistical program R, although researchers may have another statistical package available to them.
How does one decide which statistical procedure is the most appropriate? What do all the pages of the printout mean?
This course is designed as an introduction to statistical design and analysis for researchers. There is emphasis on understanding the concepts of statistical procedures (with a minimum of mathematics, although some will be discussed) and on interpreting computer output. This course is designed to help you, the researcher. It is helpful if you have done an undergraduate statistics subject, although this course can serve as a first introduction or a refresher.
Instructions on how to obtain computer printouts will be provided with an emphasis on interpreting the computer printout (most packages produce similar printouts). There will be plenty of practical work throughout the course.
Do you need to have previous experience using the program R?
It would be very helpful if you have some basic knowledge of R. We recommend our one-day course, Introduction to R (see above), which runs just prior to this course. However, previous experience with statistical packages like SAS, SPSS or Stata and some basic programming skills are also helpful.
Please note that R and R Studio are free to use and are both available for Windows/Mac/Linx platforms. The links are provided below:
You can play around with the software before deciding on the course. Here is a really good website with some introductory notes for doing statistics in R.
Course Outline
Types of experiments, scales of measurement, which method to use.
In this course, the statistical software package used is R. We do not use or demonstrate SPSS.
Summarising and Graphing Data
Ways of presenting data (histograms, boxplots), measures of centre and spread, analysing tables, correlation, and confidence intervals.
Comparing Groups
Hypothesis testing concepts - power, significance, P-value. Comparing two groups (t-tests, Wilcoxon). Comparing many groups (with one treatment factor) - one-way ANOVA or Kruskal-Wallis - multiple comparison tests.
Finding Relationships
Correlation, predicting relationships (regression - simple).
Other topics covered
Non-Normal data - transformations and non-parametric tests. If time is available, we will briefly discuss sample size and power.
Duration: 2 days, 9.00am to 5.00pm daily
Dates: 26 - 27 September, 2017
UNSW students: $200
UNSW staff: $400
External: $1,000
Bring your own laptop computer.

This Short Course is based on intellectual property developed by the School of Mathematics & Statistics.

Introduction to R

25 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 open-source software, and RStudio, which is a versatile, user-friendly interface for using R.
It is very useful to do this course before our introductory statistics course, Introductory Statistics for Researchers (see below).
This one-day 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 self-paced and focussed on developing practical skills.
Course Outline
We will cover:
  • calculations in R - addition, subtraction, multiplication, division, exponentiation (raising something to a power)
  • variables - how to store values so you can easily reuse them
  • types of data - different types of data structures and how to store them (e.g. numbers, text and Boolean (TRUE or FALSE) values)
  • organising R code and data so you can easily reuse them at a later date (script files and working directories)
  • efficient ways to create patterns of numbers
  • logical operators useful for manipulating data (e.g. <, >, etc.)
  • handling "spreadsheets" in R (matrices and data frames)
  • adding comments to your code so people you share code with can easily follow it
  • using inbuilt R Help files and other help resources
Duration: 1 day, 9.00 am to 5.00 pm 
Date: Monday 25 September, 2017
UNSW students: $100
UNSW staff: $200
External: $500

Introduction to regression modelling in R

19-21 June, 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 three-day short course is aimed at applied researchers with prior experience using R and familiar with introductory statistics tools - you should know about the t-test, 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 13 June.  If you need to revise introductory statistics material, you should attend the Introductory Statistics course on 14-15 June prior to taking the regression course, which will take such material as assumed knowledge.



NOTE: If you have not used R before, we strongly recommend you attend the Introduction to R course on June 13.
Make sure you bring your own laptop! We will sort out internet access for you.
The following topics will be covered during the course:


Linear regression
Simple linear regression, including assumptions, influential observations and inference
Equivalence of two-sample t-test and linear regression
Linear models with multiple predictor variables
Multiple regression
Analysis of variance (ANOVA), including multiple comparisons
More linear models
Paired and blocked designs
Factorial experiments
Interactions in regression
Model selection
Information criteria (AIC, BIC)
Penalised estimation (LASSO)
Mixed effects models
Random effects
Linear mixed effects models
Inference for mixed models, including likelihood ratio tests, parametric bootstrap, hypothesis tests, confidence intervals
Correlated random effects
Wiggly models
Spline smoothers, including diagnostics and interactions
Generalised linear models (GLMs)
Examples of GLMs
Fitting GLMs and checking assumptions, including mean-variance relationship
Extensions - offsets, zero-inflated models



  • UNSW students $300
  • UNSW staff and other UNSW-affiliated people $600
  • People with no UNSW affiliation $1,500
Course fees include morning and afternoon teas and lunches on all days.
Bring your own laptop computer.

Model-based multivariate analysis of abundance data using R

22-26 November, 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 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.

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.
UNSW students: $400
UNSW researchers: $700
External: $1,000


Experimental Design

20 April, 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.


  • Research questions: How to ask well-defined research questions

  • Populations: How to infer properties of populations from samples

  • Manipulative experiments: Sampling design to infer causal relationships

  • Randomisation: How to collect independent samples for valid inference

  • Control: Importance of comparing to a control group, properties of good controls

  • Sample size: Determining how much data to collect to answer the question

  • Reducing variability: How blocking and statification can reduce variability and improve power

  • Pilot study: How pilot studies can save time and money

Practical Component

  • How to randomise – Random allocation of subjects to treatments and random sampling in space

  • Simple power analysis – How much data do we need to answer the question if interest

  • We will use free online tools, Excel and G*Power

Morning tea, lunch and afternoon tea are included in the workshop fee.

Participants will need to bring their own laptop with Excel (or equivalent) and G*Power to the workshop.

UNSW students: $100
UNSW researchers: $200
External (no UNSW affiliation): $500