Statistics for Research Workers using R and R Markdown

Wednesday, 22 November to Wednesday, 29 November 2023 - 6 days

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 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.

Feedback:

"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

Course structure:

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 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.

Prerequisites:

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.

Course presenters:

Professor Ian Gordon, the Director of the Statistical Consulting Centre and Associate Professor Sue Finch, who have given this and many similar courses previously.

Venue:

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.