Elementary statistics: basic probability theory, most frequent univariate and multivariate distributions, hypothesis testing (location, dispersion and distribution tests). Analysis of variance: one-way, two-way and multi-way ANOVA (fixed effect models, with and without interaction). Regression analysis (least-squares method): linear models (straight line, multiple and polynomial regression, tests of significance of regression parameters, confidence intervals, weighted regression, goodness-of-fit test), non-linear regression (basic concepts and methods). Optimization: the Simplex method. Data smoothing and filtering. Cluster analysis. Principal components analysis, principal components regression, partial least squares, factor analysis. Non-parametric statistics: location, correlation and distribution tests, analysis of variance.
LEARNING OUTCOMES:
1. To describe and explain basic principles of probability theory.
2. To describe most frequent univariate and multivariate distributions. To describe and apply location and dispersion statistical tests. To carry out error-propagation calculations.
3. To describe and apply one-way, two-way and multi-way analysis of variance (fixed effect models, with and without interaction).
4. To explain basic principles of regression analysis (least-squares method) and apply this method in the cases of linear models (straight line, multiple and polynomial regression).
5. To describe iterative methods used for the determination of regression parameters in non-linear regression analysis, and to apply this technique.
6. To use goodness-of-fit criteria and tests.
7. To explain and apply Simplex optimization method.
8. To discriminate univariate and multivariate data analysis. To carry out multivariate regression analysis.
9. To explain basic principles of principal components analysis and related techniques. To name the problems in data analysis which can be solved by using these techniques.
10. To analyse the data by means of the above-mentioned chemometric methods and commercially available softwares.
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- 1. D. L. Massart, B. G. M. Vandeginste, L. M. C. Buydens, S. de Jong, P. J. Lewi, J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Part A, Elsevier, Amsterdam 1997.
2. B. G. M. Vandeginste, D. L. Massart, L. M. C. Buydens, S. de Jong, P. J. Lewi, J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Part B, Elsevier, Amsterdam 1998.
3. N. R. Draper, H. Smith: Applied Regression Analysis, Wiley, New York 1976 (1. izd.), 1981 (2. izd.).
4. B. E. Cooper, Statistics for Experimentalists, Pergamon Press, Oxford 1969.
- B. G. M. Vandeginste, D. L. Massart, L. M. C. Buydens, S. de Jong, P. J. Lewi, J. Smeyers-Verbeke: Handbook of Chemometrics and Qualimetrics, Part B, Elsevier, Amsterdam 1998.
- N. R. Draper, H. Smith: Applied Regression Analysis, Wiley, New York 1976. (1. izd.), 1981. (2. izd.).
- B. E. Cooper, Statistics for Experimentalists, Pergamon Press, Oxford 1969.
- W. R. Dillon, M. Goldstein: Multivariate Analysis - Methods and Aplplications, Wiley, New York 1984.
- D. L. Massart, B. G. M. Vandeginste, S. N. Deming, Y. Michotte, L. Kaufman: Chemometrics: A Textbook, Elsevier, Amsterdam 1988.
- S. N. Deming, S. L. Morgan: Experimental Design: A Chemometric Approach, Elsevier, Amsterdam 1987.
- I. Pavlić, Statistička teorija i primjena, 4. izd., Tehnička knjiga, Zagreb 1988.
- W. H. Press, B. P. Flannery S. A. Teukolsky, W. T. Vetterling: Numerical Recipes, Cambridge University Press, Cambridge 1986. (i kasnija izdanja).
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