Coefficient of Skewness Calculator


About Coefficient of Skewness Calculator (Formula)

The Coefficient of Skewness Calculator is a tool used to quantify the asymmetry or skewness of a probability distribution. It helps determine the degree and direction of skewness in a dataset, providing insights into the shape of the distribution.

The formula for calculating the Coefficient of Skewness (CS) is:

CS = 3 * (Mean – Median) / Standard Deviation

Let’s explain each component of the formula:

  1. Coefficient of Skewness (CS): This represents the measure of skewness in the dataset. A positive value indicates a right-skewed distribution, where the tail is elongated towards the higher values. A negative value indicates a left-skewed distribution, where the tail is elongated towards the lower values. A CS value close to zero suggests a nearly symmetrical distribution.
  2. Mean: The mean is the average of all the values in the dataset.
  3. Median: The median is the middle value in a dataset when it is arranged in ascending or descending order. It represents the value that separates the higher and lower half of the data.
  4. Standard Deviation: The standard deviation is a measure of the dispersion or spread of the dataset around the mean. It quantifies how much the individual data points deviate from the mean.

The Coefficient of Skewness is a dimensionless value, and its magnitude helps in understanding the extent of skewness in the data. By analyzing the skewness, researchers, statisticians, and analysts can gain valuable insights into the characteristics of the dataset and make informed decisions in various fields, such as finance, economics, and social sciences.

A perfect symmetrical distribution has a skewness coefficient of zero, while positive and negative values indicate right-skewed and left-skewed distributions, respectively. The Coefficient of Skewness Calculator is a valuable tool for data analysis, helping users understand the nature of data distributions and assess the impact of outliers or extreme values on the dataset’s shape.

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