@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/general_linear_models>
  skos:prefLabel "general linear models"@en ;
  a skos:Concept ;
  skos:narrower <https://concepts.sagepub.com/social-science/concept/least_squares_criterion> .

<https://concepts.sagepub.com/social-science/concept/regression_analysis>
  skos:prefLabel "regression analysis"@en ;
  a skos:Concept ;
  skos:narrower <https://concepts.sagepub.com/social-science/concept/least_squares_criterion> .

<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/least_squares_criterion> .

<https://concepts.sagepub.com/social-science/concept/least_squares_criterion>
  skos:definition "The rule that using the ∗mean to predict the scores in a distribution results in predictions that are most accurate, with “accurate” in this case indicating that using the mean yields the smallest possible sum of squared ∗errors (or squared ∗deviation scores). In ∗regression analysis, it is called ∗ordinary least squares (OLS), which is a method or criterion for calculating the ∗regression equation (or drawing the regression line) that best summarizes or fits a distribution. [Source: <a href=\"https://methods.sagepub.com/reference/dictionary-of-statistics-methodology/n1041.xml\" target=\"_blank\" data-id=\"to-srm\">Dictionary of Statistics &amp; Methodology; Least Squares Criterion (or Principle)</a>]"@en ;
  skos:broader <https://concepts.sagepub.com/social-science/concept/regression_analysis>, <https://concepts.sagepub.com/social-science/concept/general_linear_models> ;
  skos:prefLabel "least squares criterion"@en ;
  a skos:Concept .