Statistics for Research Workers using R and R Markdown
Starts: Monday, 20 February 2023 to Friday, 17 March 2023 - over 4 weeks
This very popular course provides an introduction to foundational statistical methods and ideas used throughout statistics and data science. It uses R statistical software with RStudio and R Markdown. You will gain experience in the use of R as part of the course, but the focus is on statistical methods including:
- Measurement and study design
- Data summaries and data visualization
- Understanding distributions: the Normal distribution and the binomial distribution
- Central Limit Theorem and its application
- Foundations of statistical inference: estimation and confidence intervals, hypothesis testing
- Simple analytic methods for numerical outcomes: paired samples and independent samples
- General linear model for numerical outcomes, including analysis of variance and linear regression
- Simple analytic methods for categorical data based on contingency tables
- General analytic method for binary outcomes: logistic regression
- Principles of the design of experiments, including determination of sample size
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 will be run online in February 2023. 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.
"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
Feedback from online course:
"Actually worked really well. I think there was a good balance of recorded and live. Plus everyone is pretty comfortable with zoom now so I think that makes a big difference. It made completing the course achievable and also very flexible which is greatly appreciated."2021
"Good as it allowed time to catch up if needed between sessions, especially as it is not 6 days straight to allow for interruptions with work and other commitments which may come up. Again, making the course very flexible and enabling you to learn at your own pace for certain sections. I think had it all been in person over 6 days I would have got lost and not gained as much as I did."2021
"Break up of the videos was good in terms of timings and concepts covered. Concepts between videos flowed nicely. Was helpful to be able to replay certain parts and pause at times to be able to keep up conceptually, which can be hard when in a live setting."2021
How useful the course will be to you.
"Extremely! I do think that the graduate research program could consider this being required coursework for students. Not having set coursework is difficult to know what you need to know and certainly doing subjects like this at the very start of the PhD is very valuable, rather than part way through."2021
Did the course meet your expectations?
"Yes, exceeded it actually. It was a lot more conceptual theory than I anticipated which was great and the R bit was literally just how you do this bit in R. Thank you."2021
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.
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 The University of Melbourne)
$30 Cancellation fee applies
The fee includes a comprehensive set of notes. A certificate on completion can be provided on request.