Postgraduate Course: The Principles of Analytical Chemistry: Sampling, Statistics, and Data Handling (CHEM10068)
Course Outline
| School | School of Chemistry |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 10 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This 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
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Entry Requirements (not applicable to Visiting Students)
| Pre-requisites |
|
Co-requisites | |
| Prohibited Combinations | |
Other requirements | None |
Course Delivery Information
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| Academic year 2021/22, Not available to visiting students (SS1)
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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 )
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| 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 |
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| Main Exam Diet S2 (April/May) | | 3:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- discuss the relative merits of different sampling strategies
- identify how to quantify and reduce sampling and overall method variance
- apply the principles of experimental design
- establish and evaluate quality assurance procedures in support of an analytical measurement.
- define accuracy and precision and calculate combinations of errors and confidence limits
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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.
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| Keywords | Not entered |
Contacts
| Course organiser | Dr Annamaria Lilienkampf
Tel: (0131 6)50 4812
Email: Annamaria.Lilienkampf@ed.ac.uk |
Course secretary | Ms Zoe Burger
Tel: (0131 6)50 7546
Email: zoe.burger@ed.ac.uk |
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