Preface

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.

**Leandro Valim de Freitas**  Petróleo Brasileiro SA – PETROBRAS São Paulo State University – UNESP

**Ana Paula Barbosa Rodrigues de Freitas** São Paulo University – USP São Paulo State University – UNESP

**Section 1** 

**Multivariate Analysis in Management** 

**Multivariate Analysis in Management** 

**Chapter 1** 

© 2012 de Freitas et al., licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 de Freitas et al., licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

**Contributions of Multivariate Statistics** 

Leandro Valim de Freitas, Ana Paula Barbosa Rodrigues de Freitas, Fernando Augusto Silva Marins, Estéfano Vizconde Veraszto,

José Tarcísio Franco de Camargo, J. Paulo Davim and Messias Borges Silva

This study aims to develop and validate multivariate mathematical models in order to

Methods heavily based on statistical and artificial intelligence as multivariate or chemometric methods have been widely used in the oil industry (KIM; LEE, KIM, 2009). Several articles have been written about applications of multivariate analysis to predict

Pasadakis, Sourligas and Foteinopoulos (2006) have used the first six principal components of Principal Component Analysis (PCA) as input variables in nonlinear modeling of oil

Pasquini and Bueno (2007) have proposed a new approach to predict the true boiling point of oil and its degree API (American Petroleum Institute) - a measure of the relative density of liquids by Partial Least Squares (PLS) and Artificial Neural Networks (ANN). Samples of mixtures oil were obtained from various producing regions of Brazil and abroad. In this application, the models obtained by the PLS method were superior to neural networks. The short time required for prediction the properties justifies the proposed of characterization

Teixeira et al. (2008) in work with Brazilian gasoline used the multivariate algorithm Soft Independent Modeling of Class Analogy (SIMCA) for clusters analysis. Aiming to quantify the amount of adulteration of gasoline by other hydrocarbons, the PLS method was applied. Finally, the models were validated internally by cross-validation algorithm and externally

monitor in real time the quality processing of derivatives in an oil refinery.

properties of oil derivatives (Santos Junior et al., 2005; Chung, 2007).

**in Oil and Gas Industry** 

Additional information is available at the end of the chapter

the oil quicker to monitor refining processes.

with an independent set of samples.

http://dx.doi.org/10.5772/54090

**1. Introduction** 

properties.
