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Unit of study_
ENVX3002: Statistics in the Natural Sciences
2024 unit information
This unit of study is designed to introduce students to the analysis of data they may face in their future careers, in particular data that are not well behaved. The data may be non-normal, there may be missing observations, they may be correlated in space and time or too numerous to analyse with standard models. The unit is presented in an applied context with an emphasis on correctly analysing authentic datasets, and interpreting the output. It begins with the analysis and design of experiments based on the general linear model. In the second part, students will learn about the generalisation of the general linear model to accommodate non-normal data with a particular emphasis on the binomial and Poisson distributions. In the third part linear mixed models will be introduced which provide the means to analyse datasets that do not meet the assumptions of independent and equal errors, for example data that is correlated in space and time. The unit ends with an introduction to machine learning and predictive modelling. A key feature of the unit is using R to develop coding skills that are become essential in science for processing and analysing datasets of ever increasing size.
Unit details and rules
Managing faculty or University school:
Life and Environmental Sciences Academic Operations
| Code | ENVX3002 |
|---|---|
| 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 |
At the completion of this unit, you should be able to:
- LO1. explain the similarities and differences between ANOVA and regression data analysis in terms of the general linear model
- LO2. demonstrate proficiency in the use of R (and interpretation of the output) for modelling data with a general linear model
- LO3. demonstrate proficiency in the use of R (and interpretation of the output) for modelling datasets that have non-normal distributions (binomial, Poisson) using a generalised linear model
- LO4. demonstrate proficiency in the use of R (and interpretation of the output) for modelling univariate relationship using non-linear functions and splines
- LO5. demonstrate proficiency in the use of R (and interpretation of the output) for modelling datasets that fail to meet the assumption of i.i.d errors (non-normality and/or correlation in space and time) by using residual maximum likelihood (REML)
- LO6. demonstrate proficiency in the use of R (and interpretation of the output) for making predictive models using tree-based models, and for assessing the quality of the models
- LO7. demonstrate proficiency in the use of R (and interpretation of the output) in analysing big data using modern computational intensive techniques.
Unit availability
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2023
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Normal day | Camperdown/Darlington, Sydney | |
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Semester 1 2023
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Normal day | Remote |
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 1 2024
|
Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
This refers to the Mode of attendance (MoA) for the unit as it appears when you’re selecting your units in Sydney Student. Find more information about modes of attendance on our website.
