.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "11_demos\python_packages\seaborn\demo_linearRegression.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_11_demos_python_packages_seaborn_demo_linearRegression.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_11_demos_python_packages_seaborn_demo_linearRegression.py:

Linear regression
====================

.. GENERATED FROM PYTHON SOURCE LINES 4-19



.. image-sg:: /11_demos/python_packages/seaborn/images/sphx_glr_demo_linearRegression_001.png
   :alt: demo linearRegression
   :srcset: /11_demos/python_packages/seaborn/images/sphx_glr_demo_linearRegression_001.png
   :class: sphx-glr-single-img


.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <seaborn.axisgrid.FacetGrid object at 0x0000013CB76D5310>





|

.. code-block:: default


    import seaborn as sns
    sns.set_theme()

    # Load the penguins dataset
    penguins = sns.load_dataset("penguins")

    # Plot sepal width as a function of sepal_length across days
    g = sns.lmplot(
        data=penguins,
        x="bill_length_mm", y="bill_depth_mm", hue="species",
        height=5
    )

    # Use more informative axis labels than are provided by default
    g.set_axis_labels("Snoot length (mm)", "Snoot depth (mm)")

.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 0 minutes  0.909 seconds)


.. _sphx_glr_download_11_demos_python_packages_seaborn_demo_linearRegression.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example


    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: demo_linearRegression.py <demo_linearRegression.py>`

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: demo_linearRegression.ipynb <demo_linearRegression.ipynb>`