Statistics for Research Workers using R and R Markdown
Scheduled for November 2021
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 might be run online or face to face in November 2021 still to be decided. The following shows the format for online delivery mode.
Online intensive courses are challenging, so we are scheduling the course over four weeks. The course will run on Mondays, Tuesdays, Thursdays and Fridays. On these days, you can expect to spend an average of 3 hours per day on the course.
There will be a mix of pre-recorded lectures, live Q&A (via Zoom) with course presenters, and Zoom tutorial sessions. Lecture material is pre-recorded to support flexibility in online learning.
On Mondays and Thursdays, pre-recorded lecture material will be provided. On average there are 2.5 hours of lectures on these days.
At 4:30 on Mondays and Thursdays, there will be live Q&A with the course presenters to follow up on any questions you have about the pre-recorded lectures.
On Tuesdays and Fridays, there will be 2-hour tutorial sessions where practical work using software can be done with the support of experienced tutors. The tutorials are planned to start at 10am.
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
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.