Computational exploratory data analysis methods include simple basic statistics and more advanced, multivariate exploratory techniques designed to identify patterns in multivariate data sets that include different methods such as: Factor analysis, Cluster analysis, Discriminant Function Analysis, etc.

This paper provides preliminary results on exploratory data analysis in cotton quality management with the application of a factor analysis approach to a spinning data base.

In this study we present the work that we have developed in a Portuguese spinning textile enterprise.

The performance, a factor analysis is used to detect the relationships between processing spinning variables, is demonstrated.

$ for developing taxonomies or systems of classification;

$ to investigate useful ways to conceptualize our group items;

$ to generate hypotheses;

$ to test hypotheses.

In multiple regression and analysis of variance, several variables are used, however one - a dependent variable - is generally predicted or explained by means of others - independent variables and covariates. These are called dependence methods.