Multivariate Analysis in Management, Engineering and the Sciences

Abstract

Recently statistical knowledge has become an important requirement and occupies a prominent position in the exercise of various professions. Every day the professionals use more sophisticated statistical tools to assist them in decision making.In the real world, the processes have a large volume of data and are naturally multivariate and as such, require a proper treatment. For these conditions it is difficult or practically impossible to use methods of univariate statistics.The wide application of multivariate techniques and the need to spread them more fully in the academic and the business justify the creation of this book. The objective is to demonstrate interdisciplinary applications to identify patterns, trends, associations and dependencies, in the areas of Management, Engineering and Sciences.The book is addressed to both practicing professionals and researchers in the field.

Metabolomics of Ulva lactuca Linnaeus (Chlorophyta) exposed to oil fuels: Fourier transform infrared spectroscopy and multivariate analysis as tools for metabolic fingerprint

“…PCA, a multivariate analysis technique is a mathematical algorithm that reduces the dimensionality of datasets by developing a smaller number of uncorrelated artificial variables, called principal components (PCs), that account for most of the variance in the observed variables. Samples can then be plotted, making possible to visualize similarities and differences between samples and whether they can be grouped (Ringner, 2008;Ami et al, 2012). Table 1 Labels for control group (C) and groups exposed to 0.001% (C1), 0.01% (C2), 0.1% (C3), and 1.0 (C4) of diesel oil (D) or gasoline (G) for 30 min (T1), 1 h (T2), 12 h (T3), and 24 h (T4).…”