Module Details

Module Code: STAT8014
Title: Environmental Statistics
Long Title: Environmental Statistics
NFQ Level: Advanced
Valid From: Semester 1 - 2019/20 ( September 2019 )
Duration: 1 Semester
Credits: 5
Field of Study: 4620 - Statistics
Module Delivered in: 1 programme(s)
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:
# Learning Outcome Description
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.
Dependencies
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
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
 
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.
ANOVA
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.
Regression
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.
Module Content & Assessment
Assessment Breakdown%
Coursework40.00%
End of Module Formal Examination60.00%

Assessments

Coursework
Assessment Type Short Answer Questions % of Total Mark 15
Timing Week 7 Learning Outcomes 1,2
Assessment Description
In-class test: experimental design
Assessment Type Project % of Total Mark 25
Timing Week 10 Learning Outcomes 1,2,3,4
Assessment Description
Analyse an environmental data set and report the results
End of Module Formal Examination
Assessment Type Formal Exam % of Total Mark 60
Timing End-of-Semester Learning Outcomes 1,2,3,4
Assessment Description
End of semester final examination
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 University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Module content delivery Every Week 2.00 2
Lab Contact Analysis using statistical software Every Week 2.00 2
Independent & Directed Learning (Non-contact) Non Contact Exercise Sheets Every Week 3.00 3
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