@prefix skos: <http://www.w3.org/2004/02/skos/core#> .
@prefix isothes: <http://purl.org/iso25964/skos-thes#> .

<https://concepts.sagepub.com/social-science/concept/conceptgroup/methods>
  a skos:Collection, isothes:ConceptGroup ;
  skos:prefLabel "methods"@en ;
  skos:member <https://concepts.sagepub.com/social-science/concept/principal_component_analysis> .

<https://concepts.sagepub.com/social-science/concept/principal_component_analysis>
  skos:definition "Principal component analysis (PCA) is a multivariate analysis technique whose goal is to reduce the dimensionality of a large number of interrelated variables. It belongs to the class of projection methods and achieves its objective by calculating one or more linear combinations of the original set of maximum variance. [Source: <a href=\"https://methods.sagepub.com/reference/encyclopedia-of-measurement-and-statistics/n356.xml\" target=\"_blank\" data-id=\"to-srm\">Encyclopedia of Measurement and Statistics; Principal Component Analysis</a>]"@en ;
  skos:exactMatch <https://id.nlm.nih.gov/mesh/D025341.html> ;
  skos:broader <https://concepts.sagepub.com/social-science/concept/statistics_as_topic> ;
  skos:prefLabel "principal component analysis"@en ;
  a skos:Concept .

<https://concepts.sagepub.com/social-science/concept/statistics_as_topic>
  skos:prefLabel "statistics as topic"@en ;
  a skos:Concept ;
  skos:narrower <https://concepts.sagepub.com/social-science/concept/principal_component_analysis> .