Coefficient of Skewness
What is the coefficient of skewness?
The coefficient of skewness is a statistical measure that describes the degree of asymmetry of a data distribution around its mean.
How is skewness classified?
Skewness can be classified as:
- Positive skew (right-skewed): Tail on the right side is longer or fatter.
- Negative skew (left-skewed): Tail on the left side is longer or fatter.
- Zero skew (symmetrical): Data is evenly distributed around the mean.
Why is skewness important?
Skewness is important because it helps identify the direction and degree of asymmetry in data, which can affect statistical analyses and interpretations.
How is the coefficient of skewness calculated?
The coefficient of skewness can be calculated using formulas like Pearson’s first and second coefficients, or more complex methods like the moment-based skewness formula.
What does a positive skewness value indicate?
A positive skewness value indicates that the data distribution has a longer or fatter tail on the right side, suggesting more data values are concentrated on the left.
Coefficient of Skewness
Coefficient of Skewness is a statistical measure that indicates the asymmetry of data around its mean, revealing whether the data is skewed to the left, right, or is symmetrical.
By identifying the direction and degree of skewness, researchers can gain insights into the underlying patterns and characteristics of the data. In this article, we will discuss all the Coefficient of Skewness i.e., Pearson’s Coefficient, Bowley’s Coefficient, and Kelly’s Coefficient.
Table of Content
- What is Skewness?
- Types of Skewness
- What is Coefficient of Skewness?
- Pearson’s First Coefficient of Skewness
- Pearson’s Second Coefficient of Skewness
- Bowley’s Coefficient of Skewness
- Kelly’s Coefficient of Skewness
- Interpreatation of Coefficient of Skewness
- FAQs
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