What Skew is
Skew is a measure of the asymmetry of a probability distribution. It is a measure of how much a distribution is shifted or “skewed” to one side or the other. Skewness can be either positive or negative, or even undefined. Positive skewness indicates that the data is skewed to the right, with the tail of the distribution extending to the right of the mean. Negative skewness indicates that the data is skewed to the left, with the tail of the distribution extending to the left of the mean.
Steps to calculate Skew:
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Collect the data.
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Calculate the mean, variance, and standard deviation of the data.
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Calculate the skewness.
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Interpret the skewness.
The formula for calculating skewness is:
Skewness = (3 * (Mean – Mode)) / Standard Deviation
where Mode is the most common value in the data set.
Examples
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Skew can be used to measure the degree of asymmetry of a distribution. For example, a left-skewed distribution would have a negative skew, while a right-skewed distribution would have a positive skew.
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Skew can be used to identify outliers in a dataset. For example, if a dataset has a high positive skew, most of the values in the dataset will be larger than the mean, indicating that there may be some outliers in the data.
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Skew can be used to compare the distributions of two datasets. For example, if two datasets have the same mean but different skews, this can indicate that their distributions are different.