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Home » Statistics Homework Help » Skewness Moments Kurtosis
Skewness Moments Kurtosis
In the measures of central tendency and variability, there are two other comparable characteristics called skewness and kurtosis that help us to understand a distribution. Two distributions may have the same mean and standard deviation but may be differ widely in their overall appearance.

Skewness can be defined as:

When a series is not symmetrical it is said to be asymmetrical or skewed. Skewness refers to the asymmetry or lack of symmetry in the shape of a frequency distribution. Measures of skewness tell us the direction and the extent of skewness. In symmetrical distribution the mean, median and mode are identical. The more the mean moves away from the mode, the larger the symmetry or skewness. A distribution is said to be skewed when the mean and the median fall at different points in the distribution, and the balance (or centre of gravity) is shifted to one side or the other-to the left or right.

The analysis of the above shows that the term skewness refers to lack of symmetry i.e. when a distribution is not symmetrical (or is asymmetrical) it is called a skewed distribution. Any measure or skewness indicates the difference between the manners in which items are distributed in a particular distribution compared with a symmetrical (or normal) distribution. If, for example, skewness is positive, the frequencies in the distribution are spread out over a greater range of values on the high value end of the curve (the right hand side) than they are on the low value end, if the curve is normal. Spread will be the same on both sides of the centre point and the mean; median and mode will have the same value.

Some of its main topics are:

1. Bowley’s coefficient of skewness
2. Karl Pearson’s coefficient of skewness
3. Moments
4. Kelly coefficient skewness
5. Measures of skewness
6. Grouping errors

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