.. _sphx_glr_api_gallery_statistics_pca:

Principal component analysis
------------------------------

Pearson's lines of closest fit and Hotelling's principal components.


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  <div id='sg-tag-list' class='sphx-glr-tag-list'></div>


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    <div class="sphx-glr-thumbnails">

.. thumbnail-parent-div-open

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    <div class="sphx-glr-thumbcontainer" tooltip="Pearson (1901) asked for the line that best fits a cloud of points when both coordinates carry error: the one minimizing the perpendicular distances, not the vertical ones of least-squares regression. It runs along the leading eigenvector of the covariance matrix. Hotelling (1933) turned the same eigenvectors into a method for many variables: replace correlated test scores by a few uncorrelated &quot;components&quot; that carry most of their variance.">

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  .. image:: /api/gallery/statistics/pca/images/thumb/sphx_glr_plot_01_pearson_hotelling_pca_thumb.png
    :alt:

  :doc:`/api/gallery/statistics/pca/plot_01_pearson_hotelling_pca`

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      <div class="sphx-glr-thumbnail-title">Pearson and Hotelling's principal component analysis</div>
    </div>


.. thumbnail-parent-div-close

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    </div>


.. toctree::
   :hidden:

   /api/gallery/statistics/pca/plot_01_pearson_hotelling_pca