Advanced Statistical Analysis Using R

The focus of this course is on learning advanced statistical methods using R.


This course is intended for those who have basic knowledge and experience with R, and would like to further advance or develop their experiences with advanced statistical methods using R. The course would also be suitable for people familiar with these statistical methods in other packages, but with no prior experience using R.

Level 3 - runs over 5 days

Dr Mark Griffin is the Director of ResearchStats, which is a Division of Insight Research Services Associated ( ResearchStats provides training and consulting in statistics for academic audiences. Mark is also an Industry Fellow with the School of Business, University of Queensland, and has established and written training materials for several of their courses in Business Analytics. Mark serves on the Executive Committee for the Statistical Society of Australia, and is Founding Chair of their Section for Business Analytics. Mark is also the Founding Chair of the Business Analytics Special Interest Group within the International Institute of Business Analysis. To date he has presented over 100 two-day and 30 five-day workshops in statistics around Australia.

About this course: 

R is a free software environment for scientific and statistical computing and graphics that runs on all common computing platforms. An active and highly skilled developer community works on development and improvement. It has become an environment of choice for the implementation of new methodology. It is at the same time attracting wide attention from statistical application area specialists. The powerful and innovative graphics abilities available in R include the provision of well-designed publication-quality plots.


The first day of this course will focus on the R software environment, the remaining days of this workshop will focus on learning advanced statistical methods with R. We will spent an almost equal amount of time in PowerPoint sessions and computer exercises. During the PowerPoint sessions the focus will be on the statistical methods with minimal discussion of computer software. During the computer exercise time you will be using R to apply the statistical methods taught in the lectures



Course syllabus: 

Day 1

The R software environment:

  • what does the R window look like,
  • help screens in R,
  • data objects and data types in R,
  • importing and exporting data from R,
  • R packages,
  • writing your own R scripts,
  • data visualisation in R.


Day 2

  • Linear, logistic and Poisson regression - Including odds ratios, incidence rate ratios, and regression diagnostics


Day 3

  • Analysis of Variance
  • Factor analysis – including factor rotations, uniqueness and commonality


Day 4

  • Mixed effects models for longitudinal and clustered data.
  • Clustering techniques – k means clustering, cluster linkage, and dendrograms
  • Missing data and multiple imputation


Day 5

  • Clustering Techniques - k means clustering, cluster linkage and dendograms


Course format: 

This course may run in a computer lab, or you may be advised to bring your own laptop with specified software.

We will let you know in advance.


Approximately half of the time in this workshop will be spent in PowerPoint seminars, and the other half will be spent in computer demonstrations and self-paced computer exercises (using datasets publicly available within R).

Recommended Background: 

This course is intended for three different demographics

  • Participants who have a basic knowledge and experience with statistical methods in R, and would like to further develop this statistical expertise.
  • Participants who have completed a basic course on statistical methods in R with ACSPRI, and would like to take their skills to the next level
  • Participants who have some familiarity with these statistical methods in other software (eg SPSS, SAS or Stata), and who wish to learn how to use these methods in the R system (potentially as new R users).


A basic knowledge of statistics is assumed. No prior knowledge of the statistical methods taught in this course or any prior experience with R is assumed, though students with prior knowledge will be better suited to tackle the more advanced topics in this workshop.


Participants must be comfortable with typing commands at the command line.


Recommended Texts: 

Maindonald, J.H. and Braun, W.J. (2010). Data Analysis and Graphics Using R. An Example-Based Approach. 3rd edn, Cambridge University Press.



Q: Do I have to have any prerequisites to do this course?

A: Yes see recommended background section.


Q: Is R really free and publicly available?

A: For sure. R has been / is being developed by an online community of statisticians and programmers around the world that have made all of their work available for the benefit of users.


Q: Can I download and install R prior to the workshop?

A: Yes, please visit It is assumed that most if not all participants will not have installed R prior to the workshop, though there may be a couple of eager participants who want to make a head start.


Participant feedback: 


Mark is an expert in statistics and his approachability and patience enables me to clarify queries that I have been trying to clarify from published knowledge. He’s really excellent in using instruction to clarify complex concepts in statistics. (Summer 2020)


Made it easy to understand (Summer 2018)


I’m going to utilize the skill I learnt here in my every day job. (Summer 2018)


Mark provided a reasonable balanced of self-guided work with interactive lectures- it was very well paced! (Winter 2017)


Got a good introduction to some important techniques in R and how they can be used in real world settings. (Winter 2017)


The instructor's bound, book length course notes will serve as the course texts.