Statistical Modelling with R
From the very beginning, R was designed for statistical modelling. Out of the box, R makes standard statistical techniques easy. This course covers the fundamental modelling techniques. We begin the day by revising hypotheses tests, before moving on to ANVOA tables and regression analysis. The class ends by looking at more sophisticated methods such as clustering and principal components analysis (PCA).
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- Basic hypothesis testing: Examples include the one-sample t-test, one-sample Wilcoxon signed-rank test, independent two-sample t-test, Mann-Whitney test, two-sample t-test for paired samples, Wilcoxon signed-rank test.
- ANOVA tables: One-way and two-way tables.
- Simple and multiple linear regression: Including model diagnostics.
- Clustering: Hierarchical clustering, k-means.
- Principal components analysis: Plotting and scaling data.
By the end of the day participants will…
- have a thorough understanding of popular statistical techniques
- have the skills to make appropriate assumptions about the structure of the data and check the validity of these assumptions in R
- be able to fit regression models in R between a response variable
- understand how to apply said techniques to their own data using R’s common interface to statistical functions
- be able to cluster data using standard clustering techniques
This course will be structured as follows:
- 9:00 — 9:15: Registration and coffee
- 9:15 — 10:30: Lecture
- 10:30 — 10:45: Coffee break
- 10:45 — 12:15: Practical 1
- 12:15 — 1:15: Lunch (not provided)
- 1:15 — 2:40: Lecture
- 2:40 — 3:00: Coffee break
- 3:00 — 4:30: Practical 2
These times are intended to give a flavour of how the course is run and are subject to change.
It will be assumed that participants are familiar with R. For example, inputting data, basic visualisation and data frames. Attending the introduction to R course will provide sufficient background. This course is suitable for all fields of work. Previous attendees include biologists, statisticians, accountants, engineers & students, i.e., anyone who uses a spreadsheet!
- The balance between lectures and practicals was good.
- Great help during the practicals.
- High quality lecture materials.