CHEMICAL DATA ANALYSIS AND MANAGEMENT

SHE Level 3
SCQF Credit Points 20.00
ECTS Credit Points 10.00
Module Code M3F121843
Module Leader Sheila Smith
School School of Computing, Engineering and Built Environment
Subject Chemical Science
Trimester
  • B (January start)

Pre-Requisite Knowledge

M2F124502 Physical Chemistry, M2F121837 Organic Chemistry 1, M2F124501 Chemical Analysis.

Summary of Content

The module will give students experience of advanced data handling techniques and interpretation of statistical data in a chemical context.

Syllabus

Practical Implementation of Measurement Uncertainty, Description of Numerical Data. Discrete Probability Distributions. Confidence Interval and Hypothesis Testing concerning the mean, variance and proportion of a single population and two populations. Analysis Of Variance. Chemometrics: background, role in chemical experimentation, case studies from selected areas of chemistry. Parameter optimisation; simplex tableau, modified Simplex, super-modified Simplex; case studies. Experimental methods, fractional experimental designs. Design of experiments: planning principles, importance of design structure to the gathering of relevant scientific information, Sampling Strategies. relation to data analysis. Calibration and bivariate analysis of data; regression, interpolation and prediction; extension to consider multivariate calibration and prediction; case studies selected from analytical spectrometry and environmental areas. The syllabus consists of a list of topics normally covered within the module. Each topic may not be dealt with in the same detail.

Learning Outcomes

On successful completion of the module the student should be able to: 1. statistically analyse the data obtained from practical experiments;2. identify key variables in experimental systems;2. determine appropriate working ranges for the variables in such systems;3. optimise the variables within the given range ;4. select an appropriate strategy for experimental design;5. identify and model key parameters in resulting multivariate data sets;6. use statistical software to aid the presentation, analysis and interpretation of practical data.

Teaching / Learning Strategy

The material covered during lectures will be reinforced and consolidated through tutorials, practical laboratory work and practical use of a commercial statistical programming package. Students will analyse real data collected through laboratory sessions where divergent thinking will be encouraged resulting in broader, deeper learning. Practical laboratory work will enhance data acquisition and manipulation skills, individual and group working skills, technical report writing skills and communication skills in general. Through the use of the managed learning environment GCU Learn, students will become more engaged, flexible and independent in their learning as there will be a wide range of learning resources available on line. In addition to the core module content, links to relevant professional bodies for the sourcing of additional reading material and from the current research in the subject area will be posted. The assessment of the students will incorporate laboratory sessions for gathering data, statistical analysis and critical evaluation of data. Students will receive individualised feedback on their performance through one-to-one contact with tutors at tutorials and marked coursework, which will reinforce the students' learning.

Indicative Reading

"Statistics and Chemometrics for Analytical Chemistry", 6th Edition J. Miller & J Miller (Prentice Hall, 2010) "Practical Statistics for the Analytical Scientist", 2nd Edition, S L R Ellison, V J Barwick, T J D Farrant (RSC Publishing, 2009) Statistics for Quality Control Chemistry Laboratory, Eamonn Mullins (RSC Publishing, 2003) Further Reading "Statistical Analysis Methods for Chemists: A Software-based Approach", W P Gardiner (The Royal Society of Chemistry, 1997) "Experimental Design Techniques in Statistical Practice: A Practical Software-based Approach", W P Gardiner and G Gettinby (Horwood Publications, 1998) Chemometrics in Analytical Spectroscopy",2nd Edition, M Adams (RSC Publishing, 2004) "Environmental Chemometrics, Principles and Modern Applications", G Hanrahan (Taylor and Francis, 2008) "An Introduction to Uncertainty in Measurement" L Kirkup & B Frenkel (Cambridge University Press, 2006) "Statistical Methods in Practice: for Scientists and Technologists", R Boddy & GL Smith (Wiley, 2009)

Transferrable Skills

The student will gain practice in using data analysis skills applied to analytical chemical data. The ability to choose appropriate software and use it effectively to find a solution to a given chemical problem. To link statistical and word processing software to enhance their proficiency in IT; - interpret and present conclusions from data analysis in written reports.

Module Structure

Activity Total Hours
Practicals (FT) 24.00
Tutorials (FT) 8.00
Assessment (FT) 20.00
Independent Learning (FT) 124.00
Lectures (FT) 24.00

Assessment Methods

Component Duration Weighting Threshold Description
TS1 2.00 60.00 35% School Exam. Learning outcomes 2,4,5,6.
CW1 n/a 40.00 35% Lab Report, 2000 words.Learning outcomes 1,3,7.