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Unit of study_
ENVX3002: Statistics in the Natural Sciences
2026 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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[6 credit points from (ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001 or DATA1901)] or [3 credit points from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3 credit points from (MATH1XX5)] |
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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. Analyse complex natural science datasets to answer substantive scientific questions, selecting and justifying appropriate statistical approaches, and critically interpreting results within a relevant disciplinary context.
- LO2. Address non-linear, high-dimensional, or poorly behaved data scenarios, evaluating alternative analytical strategies when standard assumptions break down and assessing the strengths and limitations of different approaches.
- LO3. Model and interpret structured, repeated, and hierarchical data commonly arising in natural science research, accounting for temporal, spatial, and experimental dependencies to draw valid scientific inferences.
- LO4. Incorporate predictive and data-driven modelling approaches where appropriate, demonstrating an understanding of their role, assumptions, and limitations in supporting scientific insight rather than replacing inference.
- LO5. Design, implement, and communicate reproducible data analyses, integrating statistical reasoning and coding practices to critically evaluate results and effectively communicate findings in professional-quality scientific reports.
Unit availability
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