Registry
Module Specifications
Archived Version 2008 - 2009
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Module Aims | |||||||||||||||||||||||||||||
Part 1.
Part 2. To introduce students to the most important and widely used statistical techniques for the evaluation of laboratory based data. The focus is to demonstrate the use of these techniques from a practical problem solving viewpoint and to acquaint them with the interpretation of the results arising from a statistical analysis. Particular emphasis will be placed on understanding the assumptions on which these statistical techniques are based and how to check or validate these assumptions. Topics covered will include significance testing, relative & fixed bias, precision, calibration- linear & weighted regression, detection of curvature, standard addition, limit of detection , repeatability /reproducibility and analysis of variance (one way) | |||||||||||||||||||||||||||||
Learning Outcomes | |||||||||||||||||||||||||||||
Part 1. Students will gain an appreciation of regulation and how it impacts on all operations in the pharmaceutical, environmental, food and other industries. They will also learn how the work of analytical scientists and chemists is affected by and important in the areas of quality control, quality assurance, validation, Good Manufacturing Practice (GMP), Good Laboratory Practice (GLP) and accreditation. This will help students to work in all regulated environments in industry. Part 2. Students should be able to evaluate a set of results and infer as to whether a statistically significant conclusion may be drawn from a data set. They should have a good understanding of the options available to them to improve the accuracy and precision and to minimise the uncertainty of results arising from the application of a particular test method. The course should foster a critical attitude towards experimental data collection and analysis. | |||||||||||||||||||||||||||||
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Assume that a 5 credit module load represents approximately 75 hours' work, which includes all teaching, in-course assignments, laboratory work or other specialised training and an estimated private learning time associated with the module. | |||||||||||||||||||||||||||||
Indicative Syllabus | |||||||||||||||||||||||||||||
Part 1.
Part 2. Computing workshop covering topics such as Data filtering and feature extraction, Curve fitting - linear regression and non-linear regression - with models that are built in to Microsoft Excel, Advanced curve fitting - fitting user-generated models to experimental data, Assessment of fit - examination and interpretation of residuals and Matrix algebra - use of matrix inversion to determine the composition of mixtures | |||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||
Recommended:
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Programme or List of Programmes | |||||||||||||||||||||||||||||
AS | BSc in Analytical Science | ||||||||||||||||||||||||||||
BSSA | Study Abroad (DCU Business School) | ||||||||||||||||||||||||||||
BSSAO | Study Abroad (DCU Business School) | ||||||||||||||||||||||||||||
ECSA | Study Abroad (Engineering & Computing) | ||||||||||||||||||||||||||||
ECSAO | Study Abroad (Engineering & Computing) | ||||||||||||||||||||||||||||
HMSA | Study Abroad (Humanities & Soc Science) | ||||||||||||||||||||||||||||
HMSAO | Study Abroad (Humanities & Soc Science) | ||||||||||||||||||||||||||||
SHSA | Study Abroad (Science & Health) | ||||||||||||||||||||||||||||
SHSAO | Study Abroad (Science & Health) | ||||||||||||||||||||||||||||
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