.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_data_analysis/manage_data_and_samples/plot_estimate_moments.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

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

        :ref:`Go to the end <sphx_glr_download_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py>`
        to download the full example code.

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

.. _sphx_glr_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py:


Estimate moments from sample
============================

.. GENERATED FROM PYTHON SOURCE LINES 7-8

In this example we are going to estimate statistical moments from a sample, eventually from an output variable of interest.

.. GENERATED FROM PYTHON SOURCE LINES 10-14

.. code-block:: Python

    import openturns as ot

    ot.Log.Show(ot.Log.NONE)








.. GENERATED FROM PYTHON SOURCE LINES 15-16

Create a sample

.. GENERATED FROM PYTHON SOURCE LINES 16-32

.. code-block:: Python


    # model f
    model = ot.SymbolicFunction(["x1", "x2"], ["x1^2+x2", "x2^2+x1"])

    # input vector X
    inputDist = ot.JointDistribution([ot.Normal()] * 2, ot.IndependentCopula(2))
    inputDist.setDescription(["X1", "X2"])
    inputVector = ot.RandomVector(inputDist)

    # output vector Y=f(X)
    output = ot.CompositeRandomVector(model, inputVector)

    # sample Y
    size = 1000
    sample = output.getSample(size)








.. GENERATED FROM PYTHON SOURCE LINES 33-34

Estimate mean

.. GENERATED FROM PYTHON SOURCE LINES 34-36

.. code-block:: Python

    sample.computeMean()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    class=Point name=Unnamed dimension=2 values=[0.928558,1.01799]
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 37-38

Estimate standard deviation

.. GENERATED FROM PYTHON SOURCE LINES 38-40

.. code-block:: Python

    sample.computeStandardDeviation()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    class=Point name=Unnamed dimension=2 values=[1.66702,1.81931]
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 41-42

Estimate variance

.. GENERATED FROM PYTHON SOURCE LINES 42-44

.. code-block:: Python

    sample.computeVariance()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    class=Point name=Unnamed dimension=2 values=[2.77895,3.30989]
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-46

Estimate skewness

.. GENERATED FROM PYTHON SOURCE LINES 46-48

.. code-block:: Python

    sample.computeSkewness()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    class=Point name=Unnamed dimension=2 values=[1.40965,1.73437]
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 49-50

Estimate kurtosis

.. GENERATED FROM PYTHON SOURCE LINES 50-51

.. code-block:: Python

    sample.computeKurtosis()





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    class=Point name=Unnamed dimension=2 values=[6.84373,7.96431]
    </div>
    <br />
    <br />


.. _sphx_glr_download_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py:

.. only:: html

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

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

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

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

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

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: plot_estimate_moments.zip <plot_estimate_moments.zip>`