` 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
============================
In this example we are going to estimate statistical moments from a sample, eventually from an output variable of interest.
.. code-block:: default
from __future__ import print_function
import openturns as ot
ot.Log.Show(ot.Log.NONE)
Create a sample
.. code-block:: default
# model f
model = ot.SymbolicFunction(["x1", "x2"], ["x1^2+x2", "x2^2+x1"])
# input vector X
inputDist = ot.ComposedDistribution([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)
Estimate mean
.. code-block:: default
sample.computeMean()
.. raw:: html
[0.923615,1.02297]
Estimate standard deviation
.. code-block:: default
sample.computeStandardDeviation()
.. raw:: html
[[ 1.65759 0 ]
[ 0.0710817 1.81269 ]]
Estimate variance
.. code-block:: default
sample.computeVariance()
.. raw:: html
[2.74761,3.2909]
Estimate skewness
.. code-block:: default
sample.computeSkewness()
.. raw:: html
[1.44617,1.73649]
Estimate kurtosis
.. code-block:: default
sample.computeKurtosis()
.. raw:: html
[7.00125,8.00153]
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 0.003 seconds)
.. _sphx_glr_download_auto_data_analysis_manage_data_and_samples_plot_estimate_moments.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_estimate_moments.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_estimate_moments.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_