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Unit of study_
ENVX2001: Applied Statistical Methods
2025 unit information
This unit builds on introductory statistics units and is targeted towards students in the life and environmental sciences. It consists of two parts and presents the statistical methods that students need to know for further study and their future careers, using applied examples from agriculture, ecology and environmental science. In the first part the focus is on experimental design. Students will learn how to analyse and interpret datasets collected from designs with more than two treatment levels, multiple factors and different blocking designs. The second part focuses on finding patterns in data using multiple regression and multivariate methods. Part two provides the foundation for the analysis of big data and machine learning. In the practicals the emphasis is on analysing and interpreting real datasets using the statistical software package R. A key feature of the unit is using R to develop coding skills that have become essential in science for processing and analysing datasets of ever-increasing size.
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 (ENVX1001 or 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. demonstrate proficiency in designing sample schemes and analysing data from them using using R
- LO2. describe and identify the basic features of an experimental design; replicate, treatment structure and blocking structure
- LO3. demonstrate proficiency in the use or the statistical programming language R to apply an ANOVA and fit regression models to experimental data
- LO4. demonstrate proficiency in the use or the statistical programming language R to use multivariate methods to find patterns in data
- LO5. interpret the output and understand conceptually how its derived of a regression, ANOVA and multivariate analysis that have been calculated by R
- LO6. write statistical and modelling results as part of a scientific report
- LO7. appraise the validity of statistical analyses used publications.
Unit availability
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