Difference between revisions of "Manuals/calci/SKEW"
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*In a distribution the left tail is more pronounced than the right tail (towards more negative values) then the function is said to have Negative Skewness. | *In a distribution the left tail is more pronounced than the right tail (towards more negative values) then the function is said to have Negative Skewness. | ||
*If a distribution is skewed to the right, the tail on the curve's right-hand side is longer than the tail on the left-hand side (towards more positive values), then the function is said to have a positive skewness. | *If a distribution is skewed to the right, the tail on the curve's right-hand side is longer than the tail on the left-hand side (towards more positive values), then the function is said to have a positive skewness. | ||
− | *In a | + | *In a Left Skewed Distribution, its <math>mean<median<mode</math> |
− | *In a | + | *In a Normal Skewed Distribution, its <math>mean=median=mode</math> |
− | *In a | + | *In a Right Skewed Distribution, its <math>mode<median<mean</math>. |
*In <math>SKEW(n_1,n_2,...), n_1</math> is required.<math>n_2,n_3,...</math> are optional. | *In <math>SKEW(n_1,n_2,...), n_1</math> is required.<math>n_2,n_3,...</math> are optional. | ||
*In calci there is no restriction for giving the number of arguments. | *In calci there is no restriction for giving the number of arguments. |
Revision as of 01:53, 21 January 2014
SKEW(n1,n2,…)
- Failed to parse (syntax error): {\displaystyle n_1,n_2,…} are numbers to calculate the skewness.
Description
- This function gives the Skewness of a distribution.
- Skewness is a measure of the degree of asymmetry of a distribution.
- A distribution(normal distribution) is symmetry ,it don't have a Skewness.
- In a distribution the left tail is more pronounced than the right tail (towards more negative values) then the function is said to have Negative Skewness.
- If a distribution is skewed to the right, the tail on the curve's right-hand side is longer than the tail on the left-hand side (towards more positive values), then the function is said to have a positive skewness.
- In a Left Skewed Distribution, its
- In a Normal Skewed Distribution, its
- In a Right Skewed Distribution, its .
- In is required. are optional.
- In calci there is no restriction for giving the number of arguments.
- The arguments can be be either numbers or names, array,constants or references that contain numbers.
- Suppose the array contains text,logicl values or empty cells, like that values are not considered.
- The equation for Skewness is defined by :
Where, is the sample standard deviation, represents a sample mean.
- This function will return the result as error when
1. Any one of the argument is non-numeric. 2. If there are fewer than three data points, or the Sample Standard Deviation is zero.
Examples
1.Array={4,9,11,10,5} SKEW(B1:B5)=-0.4369344921493 2.Array={0,29,41,18,4,38} SKEW(A1:A6)=-0.21921252920 3.Array={-5,11,18,7} SKEW(C1:C4)=-0.715957010 4.Array={4,5,2,5,6,8} SKEW(C1:C6)=0 5.Array={1,2,3,5,6,11} SKEW(A1:A6)=1.16584702768
See Also