Linear models form the bedrock of many real-world data analyses. They are versatile, interpretable and easily implemented. This unit provides an overview of two of the most common methods of statistical analysis of data: analysis of variance and regression. You will generate data visualisation and diagnostics plots to interpret and discover the limitations of linear models and identify when more complex approaches may be needed. You will learn to code your analyses and perform reproducible research using the open source statistical package R. A key component of this unit involves generating visualisations, estimating and selecting appropriate linear models using your data. By doing this unit you will learn how to generate, interpret, visualise, discover and critique linear models applied to your original research.
2 x 2-hr computer labs
2 x online quizzes (12.5% each, total 25%), oral presentation (30%), written report (45%)
Faraway, J. (2014). Linear models with R. Second Edition. Chapman and Hall/CRC.
Exploratory data analysis, sampling, simple linear regression, t-tests and confidence intervals. Ability to perform data analytics with coding, basic linear algebra. E.g. DATA1001 and OLET5606 (Data wrangling).
DATA2002 or DATA2902 or ENVX2001