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Advanced Statistical Analysis Using R
The focus of this course is on learning advanced statistical methods using R.
Instructor
Dr Mark Griffin is the Director of Insight Social Research & Statistics (https://www.insightrsa.com/industry-social-research). Insight focuses on research methodologies (including survey design and statistics) for public health, monitoring and evaluation for government and non-government organizations, and academic research. Insight has a secondary interest in providing IT services (as a Microsoft Business Partner).
Insight is based at the Gold Coast Health and Knowledge Precinct. The Precinct contains Griffith University Gold Coast, the Gold Coast University Hospital, the Gold Coast Private Hospital, and the Cohort and Lumina tech parks. Insight provides research, consulting, training, and IT support services for clients across the Precinct and for the broader international community.
To date he has presented over 100 two-day and 40 five-day workshops in statistics around Australia
Course Level
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.
This course is intended for those who have basic knowledge and experience with R, and would like to learn advanced statistical methods. The course is also suitable for people familiar with these statistical methods using other software, but with no prior experience using R.
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. At the start of each session of computer exercises Mark will perform the first exercise in each set on his own computer demonstrating to the class the use of the software and the statistical results obtained.
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
- Introduction to linear regression
Day 2
- Regression diagnostics
- Logistic and Poisson regression - Including odds ratios, incidence rate ratios
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
Day 5
- Missing data and multiple imputation
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.
Maindonald, J.H. and Braun, W.J. (2010). Data Analysis and Graphics Using R. An Example-Based Approach. 3rd edn, Cambridge University Press.
The instructor's bound, book length course notes will serve as the course texts.
Outstanding sessions and content throughout. I'm very grateful to have his slides and exercises as a future methods resource.
I wanted to say how much I’ve enjoyed Mark's course and his teaching style
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.
Made it easy to understand
I’m going to utilize the skill I learnt here in my every day job.
Mark provided a reasonable balanced of self-guided work with interactive lectures- it was very well paced!
Got a good introduction to some important techniques in R and how they can be used in real world settings.