#REQUEST.pageInfo.pagedescription#

Site Navigation

STAT8013 - Chemometrics

banner1
Title:Chemometrics
Long Title:Chemometrics
Module Code:STAT8013
 
Duration:1 Semester
Credits: 5
NFQ Level:Advanced
Field of Study: Statistics
Valid From: Semester 1 - 2019/20 ( September 2019 )
Module Delivered in 3 programme(s)
Next Review Date: March 2023
Module Coordinator: David Goulding
Module Author: Catherine Palmer
Module Description: This module deals with statistical methods used to extract useful information from chemical data.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 Explore data sets and establish a data analysis protocol for chemical data.
LO2 Recognise experimental design models and analyse associated sets of data.
LO3 Perform correlation and regression analysis.
LO4 Interpret the results of statistical analyses performed by a software package 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
Exploratory data analysis: graphical and numerical methods to explore categorical and continuous data sets, outlier detection, missing values, testing of assumptions and transformation of variables. Model fitting and model interpretation. Model diagnostics.
ANOVA
Fundamentals of analysis of variance, partition of sum of squares, mean squares, F ratios, post-hoc testing.
Design of Experiments
Observational (vs) experimental data. The fundamentals of experimental design. One-way and two-way ANOVA, randomised block design, full-factorial design and post-hoc testing.
Regression
Simple linear regression and an introduction to multiple linear regression. Assumptions, collinearity, interpreting coefficients, model fitting, model diagnostics.
Software Analysis
The use of statistical software in the interpretation and analysis of chemical spectral data, based on the application of the various statistical procedures dealt with in the module, will be illustrated through a suitable package e.g. Minitab, R, SPSS.
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 a 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 Formal Lecture 2.0 Every Week 2.00
Lab Analysis of spectral data case studies using statistical software 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Formal lecture 2.0 Every Week 2.00
Lab Analysis of spectral data case studies using statistical software 2.0 Every Week 2.00
Independent & Directed Learning (Non-contact) Study, Solving sample problems 3.0 Every Week 3.00
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • James Miller and Jane Miller 2018, Statistics and Chemometrics for Analytical Chemistry, 7th Ed., Pearson [ISBN: 978-129218671]
  • Montgomery, D.C. & Runger G.C. 2014, Applied Statistics and Probability for Engineers [ISBN: 978-1-118-744]
Supplementary Book Resources
  • Matthias Otto 2016, Chemometrics: Statistics and Computer Application in Analytical Chemistry, Wiley [ISBN: 9783527340972]
  • Murray R Spiegel and Larry J Stephens 2017, Schaum's Outline of Statistics, McGraw-Hill [ISBN: 978-126001146]
  • Douglas C. Montgomery 2004, Introduction to Statistical Quality Control [ISBN: 0471656313]
Recommended Article/Paper Resources
Other Resources
 

Module Delivered in

Programme Code Programme Semester Delivery
CR_SCHQA_8 Bachelor of Science (Honours) in Analytical Chemistry with Quality Assurance 7 Mandatory
CR_SESST_8 Bachelor of Science (Honours) in Environmental Science and Sustainable Technology 7 Elective
CR_SINEN_8 Bachelor of Science (Honours) in Instrument Engineering 7 Elective

Cork Institute of Technology
Rossa Avenue, Bishopstown, Cork

Tel: 021-4326100     Fax: 021-4545343
Email: help@cit.edu.ie