|Module title||Basics of Chemometrics|
|Module lecturer||prof. zw. dr hab. Marek Kręglewski|
|Lecturer position||profesor zwyczajny|
|Faculty||Faculty of Chemistry|
Module aim (aims)
The course gives an introduction to practical and theoretical chemometric methods.
The student should be able to perform basic experimental design, analysis of variance, principal component analysis, multivariate regression and basic methods within cluster analysis and classification. The statistical software will be introduced to students.
Pre-requisites in terms of knowledge, skills and social competences (where relevant)
Week 1: What is Chemometrics? Introduction to Statistical software.
Week 2: Calibration methods: regression and correlation.
Week 3: Experimental design. Two-way Analysis of Variance
Week 4: Classification. Similarity. Data Transformation (scaling, standardization).
Week 5: Distance calculation between objects. Euclidean distance, the K-nearest neighbour method (KNN), the K-means method.
Week 6: Principal Component Analysis (PCA).
Week 7: Cluster Analysis (CA).
Week 8: Dendrograms.
Week 9: Multiple Linear Regression.
Week 10: Summary and exam.
1. Alexey L. Pomerantsev, Chemometrics in Excel, Wiley 2014.
2. J. N. Miller, J.C. Miller, Statistics and Chemometrics for Analytical Chemistry, Pearson 2010.