How to describe distribution?
Skewness 🧵
Skewness 🧵
Skewness is a measure of the asymmetry of a distribution.
Distribution is symmetric if it looks the same to the left and right of the center point.
Skewness differentiates extreme values in one versus the other tail.
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Distribution is symmetric if it looks the same to the left and right of the center point.
Skewness differentiates extreme values in one versus the other tail.
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The formula to get the skewness:
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Left skew / Negative skew:
Negative values for the skewness indicate data that are skewed left - the left tail is long relative to the right tail.
The mean of a left-skewed distribution is almost always less than its median.
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Negative values for the skewness indicate data that are skewed left - the left tail is long relative to the right tail.
The mean of a left-skewed distribution is almost always less than its median.
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Right skew / Positive skew:
Positive values for the skewness indicate data that are skewed right - the right tail is long relative to the left tail.
Here the mean is almost always greater than the median.
Because extreme values affect the mean more than the median.
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Positive values for the skewness indicate data that are skewed right - the right tail is long relative to the left tail.
Here the mean is almost always greater than the median.
Because extreme values affect the mean more than the median.
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Zero skew:
When a distribution has zero skew, it is symmetrical. Its left and right sides are mirror images.
The skewness for a normal distribution is zero.
The mean = median
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When a distribution has zero skew, it is symmetrical. Its left and right sides are mirror images.
The skewness for a normal distribution is zero.
The mean = median
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Pandas has a built-in method to calculate the skewness of the data.
Since the value is negative, the data is skewed to the left - the left tail is slightly longer than the right tail.
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Since the value is negative, the data is skewed to the left - the left tail is slightly longer than the right tail.
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That's it for today.
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I hope you've found this thread helpful.
Follow me @levikul09 for more.
Like/Retweet the first tweet below for support, Thanks 😉
7/8
https://twitter.com/levikul09/status/1715671552254189727
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