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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">JDS</journal-id>
      <journal-title-group>
        <journal-title>Journal of Data Science</journal-title>
      </journal-title-group>
      <issn pub-type="epub">1680-743X</issn>
      <issn pub-type="ppub">1680-743X</issn>
      <publisher>
        <publisher-name>SOSRUC</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="publisher-id">020106</article-id>
      <article-id pub-id-type="doi">10.6339/JDS.2004.02(1).122
</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>Interpretation of Epidemiological Data Using Multiple Correspondence Analysis and Log-linear Models</article-title>
      </title-group>
      <contrib-group>
        <contrib contrib-type="author">
          <name>
            <surname>Panagiotakos</surname>
            <given-names>Demosthenes B.</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_000"/>
        </contrib>
        <aff id="j_JDS_aff_000">Technological Educational Institute of Piraeus</aff>
        <contrib contrib-type="author">
          <name>
            <surname>Pitsavos</surname>
            <given-names>Christos</given-names>
          </name>
          <xref ref-type="aff" rid="j_JDS_aff_001"/>
        </contrib>
        <aff id="j_JDS_aff_001">University of Athens</aff>
      </contrib-group>
      <volume>2</volume>
      <issue>1</issue>
      <fpage>75</fpage>
      <lpage>86</lpage>
      <permissions>
        <ali:free_to_read xmlns:ali="http://www.niso.org/schemas/ali/1.0/"/>
      </permissions>
      <abstract>
        <p>Abstract: In this work we present a combined approach to contingency tables analysis using correspondence analysis and log-linear models. Several investigators have recognized relations between the aforementioned method ologies, in the past. By their combination we may obtain a better under standing of the structure of the data and a more favorable interpretation of the results. As an application we applied both methodologies to an epi demiological database (CARDIO2000) regarding coronary hert disease risk factors.a</p>
      </abstract>
    </article-meta>
  </front>
</article>
