OpenBB/website/content/current/platform/reference/quantitative/normality.md

title, description, keywords

title	description	keywords
normality	Learn about normality statistics and their significance in data analysis. Discover different techniques such as kurtosis, skewness, Jarque-Bera, Shapiro-Wilk, and Kolmogorov-Smirnov for evaluating normality in time series data. Explore how these tests can help determine if a data sample follows a normal distribution.	normality statistics kurtosis skewness Jarque-Bera Shapiro-Wilk Kolmogorov-Smirnov time series data target column normality tests

import Tabs from '@theme/Tabs'; import TabItem from '@theme/TabItem';

Get Normality Statistics.

• Kurtosis: whether the kurtosis of a sample differs from the normal distribution.
• Skewness: whether the skewness of a sample differs from the normal distribution.
• Jarque-Bera: whether the sample data has the skewness and kurtosis matching a normal distribution.
• Shapiro-Wilk: whether a random sample comes from a normal distribution.
• Kolmogorov-Smirnov: whether two underlying one-dimensional probability distributions differ.

Parameters

data : List[Data] Time series data. target : str Target column name.

Returns

OBBject[NormalityModel] Normality tests summary. See qa_models.NormalityModel for details.

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Parameters

This function does not take standardized parameters.

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Returns

This function does not return a standardized model

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