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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. |
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©National Cotton Council, Memphis TN |
Document last modified XXXXXX, XXX XX 2001
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