Chemometrics and Statistics

Course manual 2023/2024

Course content

In this course general aspects of chemometrics and statistics applied for analytical methods will be treated. Parameters to describe the quality of analytical methods (e.g. accuracy and precision, sensitivity, selectivity, robustness) will be defined. Basic statistical methods applied to modern analytical instrumentation will be discussed. These include data exploration and visualization, statistical inference, hypothesis testing, and calibration, applied to univariate and multivariate data.

In addition, an important component of the course is signal processing and the student will learn how to find and process useful information from signals obtained by instrumental analytical techniques.

Attention will also be given to design-of-experiments and validation procedures. One of the main objectives of the course is to acquire the skills for adequate software handling for data analysis using a higher programming language.

Study materials

Literature

• B.W.J. Pirok & P.J. Schoenmakers, Analytical Separation Sciences, 2024, Royal Society of Chemistry, Chapter 9: Data Analysis (provided as PDF through Canvas)

• OPTIONAL: J.N. Miller and J.C. Miller, 'Statistics and Chemometrics for Analytical Chemistry, Pearson', 6th edition or later.

Practical training material

• Exercises will be supplied through Canvas

Software

• Julia

• GPower

Objectives

• Classify statistical analysis methods.
• Propose suitable methods to data processing.
• Set-up an experimental design and interpret the experimental results.
• Evaluate whether the applied statistical method led to a useful answer to the analytical question.
• Examine the quality of analytical methods (e.g. accuracy and precision, sensitivity, selectivity, robustness).
• Find the main characteristics of signals obtained by instrumental analytical techniques.
• Write scripts to perform statistical computations.
• Defend the implication of the choice of statistical method.

Teaching methods

• Lecture
• Seminar
• Laptop seminar
• Computer lab session/practical training
• Self-study

The lectures deliver new theoretical concepts. Students will be able to apply and practice these concepts in the tutorials using the special exercises. The exercises are intended for self-study. Numerical answers will be provided on Canvas to help students evaluating their progress. In special computer lab sessions, students will acquire further help in developing limited programming skills to apply all theoretical concepts to real data that they can encounter in their career.

The Take-Home Exam (E1) is meant to reward students for practicing homework and preparing the Final Written Exam (E2), as well as to alleviate pressure from the latter summative assessment.

Learning activities

Attendance

This programme does not have requirements concerning attendance (TER part B).

Additional requirements for this course:

Attendance is not mandatory to the lectures and tutorials. However, the course is designed with the assumption that students participate. It is thus strongly encouraged to participate.

Assessment

Inspection of assessed work

The Take-Home Exam (E1) will be graded and evaluated through Canvas. Global feedback will be provided by the examining lecturers on what components were executed well and those that lacked substance to acquire the full points. Students can immediately after the grade is announced inspect this feedback through the Canvas assignment tool.

During the QA session in Week 7, the entire take-home exam will be discussed and students can ask questions about their individual assignments. Students that have questions about their specific take-home exam can of course contact the course team by e-mail once the grade is announced within 7 days (to ensure enough time towards the final written exam).

Assignments

The Take-Home Exam (E1) commences for 7 days starting in Week 5 from March 7, 17:00 until March 14, 13:00. This exam contains several questions that students must solve using the skills they acquired during the course. Each students is assigned a unique exam, but students are encouraged to collaborate and help each other to promote peer-to-peer learning. This exam was designed to (i) reward students for preparing for the final written exam, (ii) help each other and themselves through peer-to-peer learning, (iii) reduce the weight of the final written exam. 

The Take-Home Exam (E1) will be graded individually and evaluated through Canvas. Global feedback will be provided by the examining lecturers on what components were executed well and those that lacked substance to acquire the full points. Students can immediately after the grade is announced inspect this feedback through the Canvas assignment tool.

The Take-Home Exam (E1) is a mandatory component of the course and counts for 50% to the final grade.

Fraud and plagiarism

The 'Regulations governing fraud and plagiarism for UvA students' applies to this course. This will be monitored carefully. Upon suspicion of fraud or plagiarism the Examinations Board of the programme will be informed. For the 'Regulations governing fraud and plagiarism for UvA students' see: www.student.uva.nl

Course structure

Contact information

Coordinator

• dr. B.W.J. Pirok