title: [The principal components analysis--method to classify the statistical variables with applications in medicine].
abstract: Based on the eigenvalues and the eigenvectors analysis, the principal component analysis has the purpose to identify the subspace of the main components from a set of parameters, which are enough to characterize the whole set of parameters. Interpreting the data for analysis as a cloud of points, we find through geometrical transformations the directions where the cloud's dispersion is maximal--the lines that pass through the cloud's center of weight and have a maximal density of points around them (by defining an appropriate criteria function and its minimization. This method can be successfully used in order to simplify the statistical analysis on questionnaires--because it helps us to select from a set of items only the most relevant ones, which cover the variations of the whole set of data. For instance, in the presented sample we started from a questionnaire with 28 items and, applying the principal component analysis we identified 7 principal components--or main items--fact that simplifies significantly the further data statistical analysis.
authors.affiliation: Facultatea de Medicina Dentară, Disciplina de Informatică Medicală si Biostatistică, Universitatea de Medicină si Farmacie G.T. Popa Iaşi.
authors.forename: Cristina Gena
authors.initials: CG
authors.lastname: Dascălu
authors.forename: Magda Ecaterina
authors.initials: ME
authors.lastname: Antohe
mesh_terms: D000328:Adult; D056808:Biostatistics; D003627:Data Interpretation, Statistical; D003832:Denture, Partial, Removable / Q000523:psychology* / Q000706:statistics & numerical data; D005260:Female; D006801:Humans; D008297:Male; D008432:Mathematical Computing; D025341:Principal Component Analysis / Q000379:methods; D011795:Surveys and Questionnaires