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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2021/2022

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DRPS : Course Catalogue : School of Chemistry : Chemistry

Postgraduate Course: The Principles of Analytical Chemistry: Sampling, Statistics, and Data Handling (CHEM10068)

Course Outline
SchoolSchool of Chemistry CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 10 (Postgraduate) AvailabilityNot available to visiting students
SCQF Credits10 ECTS Credits5
SummaryThis course will provide training in the foundational principles of analytical chemistry. The considerations required for developing a successful unbiased sampling strategy will be discussed; as well as statistical methods for the processing of a range of data. The course forms a part of the curriculum for any student enrolled on the PGT MSc degree course in Analytical Chemistry.
Course description The course consists of a blend of lectures, tutorials and workshops, which deal with the key concepts of analytical chemistry and data sampling and statistics. Students will be assessed on their performance in problem-based coursework. The course topics include:

An introduction to Analytical Chemistry -
- Importance of and areas of applications of qualitative/quantitative analyses
- The Analytical Process
Sampling -
- Sampling and sampling strategies: random, systematic, judgmental. Temporal factors in sampling.
- Principles of Quality Assurance, Quality Assessment and Quality Control; use of standards, calibration, blanks, controls, certified reference material and traceability, spikes and duplicates.
- Multifactorial Experimental Design. Design of Experiments (DoE)
Statistical Methods -
- Parametric versus non-parametric methods.
- Precision, accuracy, and random and systematic error; propagation of errors.
- Data Distributions. The normal and log-normal distributions; mean, median, range and standard deviation; confidence limits of the mean; use of significant figures.
- Analysis of non-normal data.
- Significance testing and null hypotheses; one and two tailed tests; type I and type II errors; parametric tests on means (paired and unpaired t-tests); parametric test on spreads (F-test).
- Non-parametric tests on medians (sign test, Wilcoxon rank sum test, Mann-Whitney U-test). Non-parametric test on spreads (Siegel-Tukey test).
- Linear and non-linear regression.
- One-way and two-way analysis of variance (ANOVA); correlation (Pearson and Spearman rank correlation coefficients); significance of the correlation coefficient.
- An introduction to multivariate analysis. Principle component Analysis, Hierarchical Cluster Analysis
- Data visualisation. Assessing the best way to represent complex data. Charts, plots, histograms, scatter plots, heat maps.

The formal lectures will be supplemented by -
- Workshops which will include the discussion and analysis of several case studies┬┐ (i) data analysis/ statistics (ii) data visualisation.
- Practical laboratory sessions
- Non-assessed data visualisation competition
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Course Delivery Information
Academic year 2021/22, Not available to visiting students (SS1) Quota:  None
Course Start Semester 1
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 100 ( Programme Level Learning and Teaching Hours 2, Directed Learning and Independent Learning Hours 98 )
Assessment (Further Info) Written Exam 0 %, Coursework 100 %, Practical Exam 0 %
Additional Information (Assessment) 100% coursework
Feedback Not entered
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May)3:00
Learning Outcomes
On completion of this course, the student will be able to:
  1. discuss the relative merits of different sampling strategies
  2. identify how to quantify and reduce sampling and overall method variance
  3. apply the principles of experimental design
  4. establish and evaluate quality assurance procedures in support of an analytical measurement.
  5. define accuracy and precision and calculate combinations of errors and confidence limits
Reading List
None
Additional Information
Graduate Attributes and Skills Develop solid foundational skill in the principles of analytical chemistry.
Understand how to plan unbiased and efficient sampling strategies.
Learn which types of statistical methods are most suitable for a wide range of data formats.
Develop the ability display complex data in a clear manner.
KeywordsNot entered
Contacts
Course organiserDr Annamaria Lilienkampf
Tel: (0131 6)50 4812
Email: Annamaria.Lilienkampf@ed.ac.uk
Course secretaryMs Zoe Burger
Tel: (0131 6)50 7546
Email: zoe.burger@ed.ac.uk
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