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Skewness Formula:
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Skewness is a measure of the asymmetry of the probability distribution of a real-valued random variable about its mean. It describes the extent to which a distribution differs from a normal distribution in terms of symmetry.
The calculator uses the skewness formula:
Where:
Explanation: This formula provides a simple way to estimate skewness using the relationship between mean, median and standard deviation.
Details: Skewness is important in statistics as it helps identify the shape of data distribution. Positive skewness indicates a longer right tail, negative skewness indicates a longer left tail, and zero skewness suggests symmetry.
Tips: Enter the mean, median, and standard deviation values. All values must be valid numbers, and standard deviation must be greater than zero.
Q1: What does positive skewness indicate?
A: Positive skewness indicates that the distribution has a longer right tail, meaning most values are concentrated on the left with some extreme values on the right.
Q2: What does negative skewness indicate?
A: Negative skewness indicates that the distribution has a longer left tail, meaning most values are concentrated on the right with some extreme values on the left.
Q3: What is considered a significant skewness value?
A: Generally, skewness values between -0.5 and 0.5 are considered approximately symmetric, values between -1 and -0.5 or 0.5 and 1 are moderately skewed, and values beyond -1 or 1 are highly skewed.
Q4: Are there other methods to calculate skewness?
A: Yes, there are other formulas such as Pearson's first coefficient of skewness and the moment coefficient of skewness, but this formula provides a good approximation.
Q5: When is this skewness formula most appropriate?
A: This formula works well for unimodal distributions that are not extremely skewed and provides a quick estimate of skewness without needing the full dataset.