========= Sign Test ========= :doc:`/WorkProcessClassifiers/GlobalAlgorithm/index` - :doc:`/WorkProcessClassifiers/OneDimensionalAlgorithm/index` *Sign Test* algorithm is a statistical method that is useful for detecting underlying trends in data. .. rubric:: Input Parameters +--------------------+--------------------------------------------+----------------------------------------+------------------------------------------------+---------+ | Parameter | Type | Constraint | Description | Remarks | +====================+============================================+========================================+================================================+=========+ | :math:`Y` | :math:`Y \in \mathbb R^N` | :math:`N \in \mathbb{N}` | Input data vector of length :math:`N` | | +--------------------+--------------------------------------------+----------------------------------------+------------------------------------------------+---------+ .. rubric:: Output Parameters +--------------------+----------------------------------------+------------+------------------------------------------+---------+ | Parameter | Type | Constraint | Description | Remarks | +====================+========================================+============+==========================================+=========+ | :math:`p` | :math:`p \in \mathbb R` | | The :math:`p` value of the test | | +--------------------+----------------------------------------+------------+------------------------------------------+---------+ | :math:`h` | Logical data type: true or false | | | | +--------------------+----------------------------------------+------------+------------------------------------------+---------+ .. rubric:: Tool Support * :doc:`/Tools/MatlabTool/index` For details refer to the online documentation of the function `'signtest' `__. .. rubric:: Single Steps using the Algorithm * :doc:`/DataPreprocessing/MathematicalComputation/HandlingDataStationarity/TestingDataStationarityWithSignTest/index` .. rubric:: References - M.\ Hollander, D.A. Wolfe, Nonparametric Statistical Methods, Hoboken, NJ: John Wiley & Sons, Inc., 1999.