Skewness, Applied Statistics

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Skewness

Meaning and Definition

 Literal meaning of skewness is lack of symmetry; it is a numerical measure which reveals asymmetry of a statistical series.

According to Paden and Lindquist, a distribution is said to be skewed if it is lacking in symmetry that is if the measure tend to pile up at one end or the other of the range to measure.

In the words of Simpson  & Kafka skewness or asymmetry is the attribute  of frequency distribution  that extends further on one side of the class   with the highest frequency that on the other.

Morris Hum burg   says skewness refers to the asymmetry or lack of symmetry in the shape of a frequency distribution. This characteristic is of particular importance in connection with judging the typicality of certain measured of central tendency.

Similarly croxton and Cowden   define it, when a series is not symmetrical. It is said to be asymmetrical or skewed.

Thus any measure of skewness indicates the difference between the manners in which items are scattered in a particular distribution compared with a normal distribution. There a may be two distributions having the same mean and the same standard deviation still their shapes may be quite different. One may be symmetrical and other may be asymmetrical.

Thus it is clear that the word skewness refers. To lack of symmetry, if a distribution is normal there would be no skewness in it, the curve drawn from the distribution would be symmetrical would be symmetrical .In case of skewness distribution the curve drawn would be tilted either to the left or towards the right.


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