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Unit of study_
ENVX3002: Statistics in the Natural Sciences
2025 unit information
ENVX3002 is designed to prepare students to analyse complex data typically encountered in the life and environmental sciences. Challenges such as non-normality, missing observations, spatial and temporal correlations, or datasets too large for standard models are addressed. Presented in an applied context, the unit emphasizes the correct analysis of authentic datasets and the interpretation of results. The course begins with the design and analysis of experimental data based on the general linear model. In the second part, students explore the generalization of the general linear model to accommodate non-normal data, focusing on binary and count data. The third part introduces linear mixed models, providing tools to analyze datasets that violate the assumptions of independent and equal errors, such as data correlated in space and time. The unit concludes with an introduction to machine learning and predictive modelling. A key feature of this unit is the use of R programming to develop coding skills that are becoming essential for future careers in the life and environmental sciences.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
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| Academic unit | Life and Environmental Sciences Academic Operations |
| Credit points | 6 |
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Prerequisites:
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ENVX2001 or STAT2X12 or BIOL2X22 or DATA2X02 or QBIO2001 |
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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None |
At the completion of this unit, you should be able to:
- LO1. Demonstrate advanced proficiency in experimental design and analysis of variance, including the application of the general linear model, through comprehensive data analyses and report writing.
- LO2. Apply generalised linear models and generalised additive models to natural science datasets, critically assessing and interpreting the results within the context of their field-specific data.
- LO3. Perform repeated measures analysis and utilise generalised linear mixed models, showcasing their ability to handle complex and dynamic datasets typical in natural sciences research
- LO4. Integrate basic machine learning techniques into their data analysis repertoire, demonstrating an understanding of their application and relevance in the natural sciences
- LO5. Develop and refine their research skills and coding skills, preparing them for their professional career, honours-level projects and/or postgraduate research, by critically evaluating data analysis methods and results, and effectively communicating their findings in detailed reports.
Unit availability
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