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https://math.answers.com/math-and-arithmetic/How_do_you_fix_skewness

How do you fix skewness? - Answers

To fix skewness in a dataset, you can apply various transformation techniques. Common methods include log transformation for right-skewed data, square root transformation, or Box-Cox transformation, which can help normalize the distribution. Additionally, you might consider using data binning or adding/removing outliers to achieve a more symmetric distribution. Always visualize the data before and after transformations to ensure the desired effect is achieved.



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How do you fix skewness? - Answers

https://math.answers.com/math-and-arithmetic/How_do_you_fix_skewness

To fix skewness in a dataset, you can apply various transformation techniques. Common methods include log transformation for right-skewed data, square root transformation, or Box-Cox transformation, which can help normalize the distribution. Additionally, you might consider using data binning or adding/removing outliers to achieve a more symmetric distribution. Always visualize the data before and after transformations to ensure the desired effect is achieved.



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https://math.answers.com/math-and-arithmetic/How_do_you_fix_skewness

How do you fix skewness? - Answers

To fix skewness in a dataset, you can apply various transformation techniques. Common methods include log transformation for right-skewed data, square root transformation, or Box-Cox transformation, which can help normalize the distribution. Additionally, you might consider using data binning or adding/removing outliers to achieve a more symmetric distribution. Always visualize the data before and after transformations to ensure the desired effect is achieved.

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      To fix skewness in a dataset, you can apply various transformation techniques. Common methods include log transformation for right-skewed data, square root transformation, or Box-Cox transformation, which can help normalize the distribution. Additionally, you might consider using data binning or adding/removing outliers to achieve a more symmetric distribution. Always visualize the data before and after transformations to ensure the desired effect is achieved.
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