Syllabus
1. Model of a signal, signal parameters, instrumentation parameters evaluation.
2. Signal processing, noise, drift, post-run calculation.
3. Analytical results, precision and accuracy, exploratory univariate data analysis, tests and graphs, statistical identification of a population, population parameters.
4. Methods of statistical analysis, univariate data, parameters, multivariate data, parameters, covariance, regression and correlation.
5. Methods of multivariate data analysis, clustering methods, hierarchical cluster analysis, resemblance coefficients.
6. Methods of multivariate data analysis, principal component analysis, latent variables, plots.
7. Discriminating analysis, canonical, classification analysis.
8. Experimental design and optimization, randomization, blocking.
9. Factorial design and optimization.
10. Factorial design, ANOVA.
11. Multifactorial design, analytical applications.
Annotation
Measurement methodology in analytical chemistry. From theory of signal to experimental design and introduction to multivariate data analysis.