In this thesis some multivariate spectroscopic methods for the analysis of solutions are proposed. Spectroscopy and multivariate data analysis form a powerful combination for obtaining both quantitative and qualitative information and it is shown how spectroscopic techniques in combination with chemometric data evaluation can be used to obtain rapid, simple and efficient analytical methods. The...

The main purpose of this paper is to describe a process for partitioning an N-dimensional population into k sets on the basis of a sample. The process, which is called 'k-means,' appears to give partitions which are reasonably efficient in the sense of within-class variance. That is, if p is the probability mass function for the population, S = {S1, S2, * *, Sk} is a partition of EN, and ui, i ...

This paper describes direct manipulation and dynamic graphics for analyzing multivariate longitudinal data. Longitudinal data measures individuals repeatedly in time, perhaps at irregular and unequal time points. There is an emphasis on studying the individual patterns as well as mean trends because we can. Static plots of individuals are messy and often unreadable because there are many overla...

Multivariate statistical methods for the analysis, monitoring and diagnosis of process operating performance are becoming more important because of the availability of on-line process computers which routinely collect measurements on large numbers of process variables. Traditional univariate control charts have been extended to multivariate quality control situations using the Hotelling T2 stat...

in bibliometric and scientometric studies, three approaches of multivariate analysis, namely factor analysis, cluster analysis and multidimensional scaling are the most used methods. this article aims to review the use and applications of these techniques.