What is Coefficient of Skewness?
The coefficient of skewness is a numerical value that quantifies the degree of skewness in a probability distribution. It provides a standardized measure of the asymmetry or lack of symmetry in the distribution, allowing for comparison across different datasets or distributions.
The coefficient of skewness can take values ranging from negative infinity to positive infinity. A value of zero indicates a perfectly symmetrical distribution, while positive values indicate positive skewness (skewed to the right), and negative values indicate negative skewness (skewed to the left).
Types of Coefficient of Skewness
There are several measures or coefficients used to quantify skewness in a distribution. The most commonly used coefficients of skewness are:
- Pearson’s Coefficient
- Pearson’s First Coefficient
- Pearson’s Second Coefficient
- Bowley’s Coefficient
- Kelly’s Coefficient
There are also other less common coefficients, such as the Grubbs’ Coefficient, Isogradient Coefficient, and Momal Coefficient, which are used in specific situations or for particular types of data distributions.
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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