STAT8014 - Environmental Statistics

Title:Environmental Statistics
Long Title:Environmental Statistics
Module Code:STAT8014
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2019/20 ( September 2019 )
Module Delivered in 1 programme(s)
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 MTU 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

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.
Fundamentals of analysis of variance (ANOVA), partition of sum of squares, mean squares, F ratios and post-hoc testing.
Design of experiments
Experimental (vs) Observational data. Fundamentals of experimental design. One-way and two-way ANOVA, randomised block design and full-factorial design.
Simple linear regression and an introduction to multiple linear regression. Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics.
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 Work40.00%
End of Module Formal Examination60.00%
Course Work
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
  • Montgomery, D.C. & Runger G.C 2014, Applied Statistics and Probability for Engineers, Wiley [ISBN: 978-1-118-744]
Supplementary Book Resources
  • 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
Other Resources

Module Delivered in

Programme Code Programme Semester Delivery
CR_SESST_8 Bachelor of Science (Honours) in Environmental Science and Sustainable Technology 7 Mandatory