Skewness
Arjun Panwar
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Arjun Panwar
Building vision based solutions | Computer Vision Engineer | Python | System Design
Skewness is among the first insight into the data set that we get from its visualization. Skewness is defined as a measure of the dataset’s symmetry. A more proper definition would be - Skewness refers to a distortion or asymmetry that deviates from the symmetrical bell curve, or normal distribution, in a set of data. If the curve is shifted to the left or to the right, it is said to be skewed.
A perfectly symmetrical data set will have zero skew. It mean, median & mode lies on the same line.
Skewness can be also defined as the degree of asymmetry observed in a probability distribution.
Measuring Skewness
Where: X = Mean value
Mo = Mode value
s = Standard deviation of the sample data
# skewness along the index axis df.skew(axis = 0, skipna = True)
Significance of Skewness
- The skewness of data helps us in creating better linear models.
- It tells us about the direction of outliers.
How Do We Transform Skewed Data?
- Power Transformation
- Log Transformation
- Exponential Transformation
Analytics Manager at EXL | Credit Risk, Logistic Models, Data Science, Machine Learning
3 年Difference between Dispersion and Skewness is very interesting, Dispersion measures the tendency of data set distributed over range in statistical analysis, where Skewness measures the asymmetry in a statistical distribution from the normal distribution.? #dataanalyst #statistics #datascience