| Title: | Environmental Statistics |
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| Long Title: | Environmental Statistics |
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| Field of Study: |
Statistics
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| Valid From: |
Semester 1 - 2019/20 ( September 2019 ) |
| Next Review Date: |
March 2023 |
| Module Coordinator: |
David Goulding |
| Module Author: |
Catherine Palmer |
| Module Description: |
This module focuses on statistical techniques used to extract useful information from environmental data. |
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| LO1 |
Explore environmental data sets and develop suitable data analysis protocols. |
| LO2 |
Recognise different experimental design models and analyse associated data sets to support environmental decisions. |
| LO3 |
Implement regression procedures for data interpretation. |
| LO4 |
Use statistical software to interpret, model and analyse environmental data and report the results. |
| Pre-requisite learning |
Module Recommendations
This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named CIT module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s). |
| 13573 |
STAT6014 |
Intro Stats for Phys. Sc. |
| 13575 |
STAT7009 |
Inferential Statistics |
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list. |
| No incompatible modules listed |
Co-requisite Modules
|
| No Co-requisite modules listed |
Requirements
This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
|
| No requirements listed |
Module Content & Assessment
| Indicative Content |
|
Data Analysis Protocol
Consolidate prior knowledge of graphical and numerical descriptive statistics to perform exploratory data analysis for both categorical and continuous data. Outlier detection, missing values, assumption testing and transformation of variables. Model fitting, interpretation and diagnostics.
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ANOVA
Fundamentals of analysis of variance (ANOVA), partition of sum of squares, mean squares, F ratios and post-hoc testing.
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Design of experiments
Experimental (vs) Observational data. Fundamentals of experimental design. One-way and two-way ANOVA, randomised block design and full-factorial design.
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Regression
Simple linear regression and an introduction to multiple linear regression. Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics.
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Laboratory Programme
Use of statistical software in examining environmental statistical data including time series data decomposition (trends, periodicity seasonality), time series smoothing techniques, forecasting and interpreting heat maps.
|
| Assessment Breakdown | % |
|---|
| Course Work | 40.00% |
| End of Module Formal Examination | 60.00% |
| Course Work |
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| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Short Answer Questions |
In-class test: experimental design |
1,2 |
15.0 |
Week 7 |
| Project |
Analyse an environmental data set and report the results |
1,2,3,4 |
25.0 |
Week 10 |
| End of Module Formal Examination |
|---|
| Assessment Type |
Assessment Description |
Outcome addressed |
% of total |
Assessment Date |
| Formal Exam |
End of semester final examination |
1,2,3,4 |
60.0 |
End-of-Semester |
| Reassessment Requirement |
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
|
The institute reserves the right to alter the nature and timings of assessment
Module Workload
| Workload: Full Time |
| Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
| Lecture |
Module content delivery |
2.0 |
Every Week |
2.00 |
| Lab |
Analysis using statistical software |
2.0 |
Every Week |
2.00 |
| Independent & Directed Learning (Non-contact) |
Exercise Sheets |
3.0 |
Every Week |
3.00 |
| Total Hours |
7.00 |
| Total Weekly Learner Workload |
7.00 |
| Total Weekly Contact Hours |
4.00 |
| This module has no Part Time workload. |
Module Resources
| Recommended Book Resources |
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- Montgomery, D.C. & Runger G.C 2014, Applied Statistics and Probability for Engineers, Wiley [ISBN: 978-1-118-744]
| | Supplementary Book Resources |
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- Steven P. Millard 2013, EnvStats - An R Package for Environmental Statistics, Springer-Verlag New York [ISBN: 978-1-4614-84]
- Dennis Wackerly, William Mendenhall, Richard L. Scheaffer 2008, Mathematical Statistics with Applications [ISBN: 978-049511081]
- Matthias Otto 2016, Chemometrics: Statistics and Computer Application in Analytical Chemistry, Chapters 2 and 3, Wiley [ISBN: 9783527340972]
- Murray R Spiegel and Larry J Stephens 2017, Schaum's Outline of Statistics, McGraw-Hill [ISBN: 978-126001146]
| | Recommended Article/Paper Resources |
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- Eurostat Statistics Explained
| | Other Resources |
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- Website: Package for Environmental Statistics,
Including US EPA Guidance
- Ebook: Eurostat 2010, Environmental statistics and accounts in
Europe
- Website: EPA Air Quality Data
- Website: US Air quality Data
- Website: Fixed point Open Ocean Observatory
network (FixO3) datasets
- Website: Minitab blog posts for learning
statistics
- Website: Wolfram Alpha
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Module Delivered in
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