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Unit of study_

ENVX2001: Applied Statistical Methods

This unit builds on introductory 1st year statistics units and is targeted towards students in the agricultural, life and environmental sciences. It consists of two parts and presents, in an applied manner, the statistical methods that students need to know for further study and their future careers. In the first part the focus is on designed studies including both surveys and formal experimental designs. Students will learn how to analyse and interpret datasets collected from designs from more than 2 treatment levels, multiple factors and different blocking designs. In the second part the focus is on finding patterns in data. In this part the students will learn to model relationships between response and predictor variables using regression, and find patterns in datasets with many variables using principal components analysis and clustering. This part provides the foundation for the analysis of big data. In the practicals the emphasis is on applying theory to analysing 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.

Code ENVX2001
Academic unit Life and Environmental Sciences Academic Operations
Credit points 6
[6cp from (ENVX1001 or ENVX1002 or BIOM1003 or MATH1011 or MATH1015 or DATA1001 or DATA1901)] OR [3cp from (MATH1XX1 or MATH1906 or MATH1XX3 or MATH1907) and an additional 3cp from (MATH1XX5)]

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.