At a glance
Frequency
Once a year or at your convenience (in-house)
Duration
6 days with one-week break
Study mode
In-person
Fees
$1100-$1485 info below
What you will learn
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.
Course topics
- Measurement and study design
- Data summaries and data visualisation
- 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
Presenters
Professor Ian Gordon
The presenter is Professor Ian Gordon, the Director of the Statistical Consulting Centre. Ian has had extensive experience over two decades in the practical application of these methods and has delivered many statistics courses to participants coming from a wide variety of backgrounds.
Associate Professor Sue Finch
Sue Finch is Deputy Director of the Statistical Consulting Centre. Despite being a statistician, she’s lost count of the number of meta-analyses she has carried out over the past 25 years in supporting PhD students, academics and clients making submissions to the Pharmaceutical Benefits Advisory Board.
Who should take this course?
This course is suitable for anyone working with data and needing to understand foundational statistical and analytical methods. People who have “forgotten all the stats they ever learnt” find this a valuable refresher course. If you need to use more advanced methods, this course provides a good basis for further learning.
What people are saying
"Amazing week, thank you. Such a worthwhile investment, course material and lessons were so comprehensive and relevant. Thank you so much."
"I highly recommend this training course, which strikes an excellent balance between comprehensive theoretical concepts and hands-on practical exercises. It provides a solid foundation for effectively using R."
"Knowledgeable, engaging presenters: Minimised the fear of stats."
"This course exceeded my expectations. 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." (Edited for clarity)
More information
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Course cost
$1485
Course cost for University of Melbourne postgraduate students
$1100
- The fee includes a comprehensive set of notes.
- A certificate on completion can be provided on request.
- All prices include GST.
- GST does not apply if paying through your University School.
- $30 cancellation fee applies.
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The course is deliberately arranged so that there is a weekend break in the middle. 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. Registration is at 9 am on the first day. Mode of delivery: face-to-face.
The six days are deliberately arranged so that there is a week's 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 am, and the final session will end at approximately 4:45 pm. 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.
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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.
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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 textbooks. 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, focusing 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.
- Moore and McCabe: Introduction to the Practice of Statistics.
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