Lecturer(s)


Meloun Milan, prof. RNDr. DrSc.

Course content

Nature of multivariate data. Exploratory data treatment. Statistical testing of multivariate data. Structure hidden in the data. Principal komponent analysis PCA. Factor analysis FA. Canonical correlation analysis CCA. Discriminant analysis DA. Logistic regression LR. Cluster analysis CLU. Multidimensional data analysis MDA. Correspondence analysis CA.

Learning activities and teaching methods

Monologic (reading, lecture, briefing), Methods of individual activities, Skills training
 unspecified
 30 hours per semester
 unspecified
 20 hours per semester
 unspecified
 40 hours per semester
 unspecified
 30 hours per semester

Learning outcomes

Multivariate data occur in all branches of science. Almost all data collected by todaýs researchers can be classified as multivariate data. Multivariate data result whenever a researcher measures or evaluates more than one attribute or characteristic of each experimental unit. These attributes or characteristics are usually called variables. Multivariate methods are extremely useful for helping researchers make sense of large, complicated, and complex data sets that consist of a lot of variables measured on large numbers of experimental units. The importance and usefulness of multivariate methods increase as the number of variables being measured and the number of experimental units being evaluated increase. The primary objective of multivariate analyses is to summarize large amounts of data by means of relatively few parameters. Multivariate analyses are often concerned with finding relationship among (1) the response variables, (2) the experimental units, and (3) both, response variables and experimental units. The main part contains the exploratory aspects of multivariate data treatment and representation. The techniques selected on the basis of practical experience with multivariate chemometrical data analysis are described. Among the more established techniques discussed in this subject are Exploratory and factor analysis, Scaling, weighting, transforms, Clustering, Principal components analysis, Covariance and correlation analysis. Classification analysis, Multivariate data scaling, Canonical correlation, Discriminant analysis, Regression model building in advanced multivariate data problems. The data explosion of recent years has not only taxed resources to physically handle and analyze all of the available information, but also required a reassessment of our approach to data analysis. Finally, the complexity of the topics being addressed and theorýs increased role in research design have combined to require more rigorous and sophisticated techniques to perform the necessary confirmatory analyses.
The subject is intended for undergraduates and graduate students in chemistry, other natural sciences, and chemical engineering, and for all who engage in applied research in all fields of chemistry. Among the more established techniques discussed in this subject are Exploratory and factor analysis, Scaling, weighting, transforms, Clustering, Principal components analysis, Covariance and correlation analysis. Classification analysis, Multivariate data scaling, Canonical correlation, Discriminant analysis, Regression model building in advanced multivariate data problems. The data explosion of recent years has not only taxed resources to physically handle and analyze all of the available information, but also required a reassessment of our approach to data analysis. Finally, the complexity of the topics being addressed and theorýs increased role in research design have combined to require more rigorous and sophisticated techniques to perform the necessary confirmatory analyses. The subject includes a large number of solved problems in the electronic classroom with personal computers, almost all of which rely on interactive examination of real laboratory data. All solved problems have a similar structure; the title of the problem is followed by the problem formulation. The DATA section contains the numerical input for the program, and the program used is named. The SOLUTION contains an explanation of all the methods used and suggests a strategy of interactive investigation of a set of data. The CONCLUSION answers the question asked in the problem.

Prerequisites

There is no special request on preliminary knowledge of statistics or mathematics. The active work with computer and software (Microsoft Office) is supposed only namely the work with the text and figures is welcomed when creating and solving tasks in the semestral assay.

Assessment methods and criteria

Written examination, Home assignment evaluation
The practical data treatment using the computerassisted interactive statistical analysis is proven by student with computation of 10 tasks and the writing a semestral work which represents 40% of the final exam. Theoretical knoledge of the computerassisted interactive statistical analysis is proven by student with the written form of the exam which represents 40% of the final exam.

Recommended literature


M. Meloun, J. Militký. KOMPENDIUM STATISTICKÉHO ZPRACOVÁNÍ DAT. Academia Praha, 2002. ISBN 8020010084.

M. Meloun, J. Militký. KOMPENDIUM STATISTICKÉHO ZPRACOVÁNÍ DAT. Academia Praha, 2006. ISBN 8020013962.

M. Meloun, J. Militký, M. Hill. Počítačová analýza vícerozměrných dat v příkladech. Academia Praha, 2005. ISBN 8020013350.

M. Meloun, J. Militký. Sbírka úloh pro Statistické zpracování experimentálních dat. Univerzita Pardubice 1996, 1996. ISBN 8071940755.

M. Meloun, J. Militký. STATISTICKÁ ANALÝZA EXPERIMENTÁLNÍCH DAT v chemometrii, biometrii, ekonometrii a v dalších oborech přírodních, technických a společenských věd, . Praha, 2004. ISBN 8020012540.

M. Meloun, J. Militký. STATISTICKÁ ANALÝZA EXPERIMENTÁLNÍCH DAT v chemometrii, biometrii, ekonometrii a v dalších oborech přírodních, technických a společenských věd,. EAST PUBLISHING Praha, 1998. ISBN 8072190032.

Meloun, M.; Militký, J.; Forina, M. Chemometrics for Analytical Chemistry, Volume 1: PCAided Statistical Data Analysis. Ellis Horwood, Chichester, 1992. ISBN 0131263765.

Meloun, M.; Militký, J.; Forina, M. Chemometrics for Analytical Chemistry, Volume 2: PCAided Regression and Related Methods. Ellis Horwood, Chichester, 1994. ISBN 0131237887.
