Registry

Module Specifications

Current Academic Year 2012 - 2013
Please note that this information is subject to change.

Module Title	Multivariate Statistical Analysis
Module Code	CS507A
School	School of Chemical Sciences
Online Module Resources
NFQ level	8	Credit Rating	2.5
Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

Description

The first part of this course will involve an introduction to statistical analysis for analytical chemistry. Students will be introduced to the importance of statistical analysis of analytical data. In the workshop, they will interpret data sets using statistical tests to quantify data quality, compare sets of data, fit models to data sets and apply computer packages to analysis of data sets. Students will also develop skills in the statistical analysis of real world analytical chemistry data through case studies and develop an awareness of Total Quality Management in chemical industry.The second part of this course will focus on specialised statistical tools, which can be used to assess scientific data. One example of a statistical tool used for this analysis is Artifical Neural Networks.

Learning Outcomes

1. Critically analyse analytical data quality using appropriate tests
2. Perform fits to data including linear/non-linear regression and assess the quality of the fit
3. Evaluate and optimise the performance of a chemical process based on statistical analysis performed
4. Critically assess data sets based on applied inter-comparison tests

Workload	Full-time hours per semester
*Type*	*Hours*	*Description*
Lecture	3	3 x 1 hour lectures
Lecturer-supervised learning (contact)	3	3 x 1 hour practical sessions using computer programmes
Assignment	3	3 x 1 hour assignments using computer programmes
Independent learning time	51.5	practising using the statistical programmes and applying them to students own data sets
Lab	2	1 x 2 hour session installing the correct versions of programmes onto student laptops
Total Workload: 62.5

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Regression Analysis.
Regression Analysis using the Built-in Options of Microsoft Excel- error bars- analysis of residuals plots- quantitative comparison of different models.

Regression Analysis using Solver.
- comparison of built-in versus user defined models- avoiding distortion of data- application to enzyme kinetics.

Transfer of Instrumental Data to Microsoft Excel.
use of ASCII files.

Filtration and Differentiation of Instrumental Data.
- moving average smoothing- Savitzky-Golay Filter- differentiation of filtered data.

Significance Testing using Microsoft Excel.
- t-test- F-test- Chi-squared, χ2- ANOVA.

Assessment Breakdown
Continuous Assessment	100%	Examination Weight	0%

Course Work Breakdown
Type	Description	% of total	Assessment Date
Computerised test/exam	3 x practical problems presented to the student, which they will have to use the skills learned during the course to complete	100%	As required

Reassessment Requirement
Resit arrangements are explained by the following categories; 1 = A resit is available for all components of the module 2 = No resit is available for 100% continuous assessment module 3 = No resit is available for the continuous assessment component
This module is category 1

Indicative Reading List

James Miller and Jane Miller: 0, Statistics and Chemometrics for Analytical Chemistry,
John Haswell: 0, Practical Guide to Chemometrics,
Jurgen W. Einax, Sabine Gei, Heinz W. Zwanziger: 0, Chemometrics in Environmental Analysis,

Other Resources

None

Array

Programme or List of Programmes

AMPD

AMPM

AMPT

BTPD