Statistics for Research Workers using R and R Markdown

Wednesday, 24 November to Wednesday, 1 December 2021

Researchers looking at a slide

This very popular course gives a basic understanding of statistical ideas and methods involved in carrying out research. It provides an introduction to R and R Markdown as the basis for reproducible research.

Statistical topics covered will include data visualisation, estimation, hypothesis testing, regression and the general linear model.

The course covers:

  • Descriptive statistics; graphs, tables, summary statistics.
  • Introduction to estimation and confidence intervals.
  • The normal distribution; means and variances of sums of random variables; the Central Limit Theorem; the normal approximation to the binomial distribution.
  • Confidence intervals for means and proportions.
  • Introduction to hypothesis testing.
  • Tests for differences in location between two populations with matched samples: sign test, Wilcoxon signed-rank test, t-test. The relationship between confidence intervals and hypothesis testing.
  • Tests for differences in location between two populations with independent samples: t-test.
  • Testing for difference in location of more than two populations. Analysis of variance (F-test), multiple comparisons.
  • Two-way classifications: analysis of variance (F-test), interaction.
  • Determination of sample size.
  • Design of experiments: randomization, blocking, replication, confounding. Standard designs.
  • Correlation and straight line regression.
  • Multiple regression.
  • Analysis of categorical data; contingency tables.


"Great overview of statistical methods for people who want to get a better understanding of how they can use data in their work." 2016

"Thank you very much to the team who ran the course. I learned a lot and it is a challenging and enjoyable experience." 2016

"…Ian and Sue are amazing in explaining complicated concepts in an easy way..." 2017

"Very engaging and helpful , well thought out." 2017

"Comprehensive, well taught statistics course that will prove incredibly useful to anyone undertaking research or analyzing research results." 2019

Course structure:

The six days are deliberately arranged so that there is a weekend break during the course. Each day will consist of four approximately equal-length sessions; the first session of the day will commence at 9:15 a.m. and the final session will end at approximately 4:45 p.m. The sessions will mix lecture presentations with practical work using software; tutorial help will be liberally available. A full set of notes will be provided.  A certificate on completion can be provided on request.


There are no formal prerequisites though it is expected that most participants will have studied mathematics at VCE level, or equivalent. Participants need to be comfortable with a limited amount of mathematical notation. The onus is on participants to check that the course suits their needs. Please do this carefully.

Reading to consider:

There are no set textbooks for SRW.  The set of notes developed by the Statistical Consulting Centre is used.  Sometimes participants ask us for additional references.  Here are some suggestions.

There are many introductory statistics text books.  It is a good idea to go to a University library or local library and browse these; you are likely to find one that suits your needs and tastes in textbooks.

The following are some you might consider:

  • Moore and McCabe: Introduction to the Practice of Statistics This was one of the first of the new generation of introductory texts, focussing more on insight and understanding, and with a good deal of enrichment material.
  • Altman: Practical Statistics for Medical Research. One of several texts designed for those in medical fields.
  • Mead, Curnow and Hasted: Statistical Methods in Agriculture and Experimental Biology. Has minimal mathematical notation.
  • Utts and Heckard: Mind on Statistics. An excellent book on broader issues of statistical literacy, with many interesting examples and case studies.

Cost and enrolment details:

The cost of the course is $1485 (incl. $135 GST).

We have a discounted rate for University of Melbourne postgraduate students of $1100 (incl. $100 GST, GST does not apply if paying through your The University of Melbourne)

$30 Cancellation fee applies

The fee includes a comprehensive set of notes.


Professor Ian Gordon
Dr Sue Finch


School of Mathematics and Statistics
Peter Hall Building
Wilson Computer Lab


Parking around campus
PTV timetable
Wheelchair accessible


T: +61 3 8344 6995


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$30 cancellation fee applies