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 left skewed distribution ,its <math>mean<median<mode</math>
+
*In a Left Skewed Distribution, its <math>mean<median<mode</math>
*In a normal  skewed distribution, its <math>mean=median=mode</math>
+
*In a Normal Skewed Distribution, its <math>mean=median=mode</math>
*In a right skewed distribution, its <math>mode<median<mean</math>.  
+
*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


References