Difference between revisions of "Manuals/calci/SKEW"

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<div style="font-size:30px">'''SKEW(n1,n2,…)'''</div><br/>
 
<div style="font-size:30px">'''SKEW(n1,n2,…)'''</div><br/>
 
*<math>n_1,n_2,…</math> are numbers to calculate the skewness.
 
*<math>n_1,n_2,…</math> are numbers to calculate the skewness.
 
  
 
==Description==
 
==Description==
*This function gives the skewness of a distribution.  
+
*This function gives the Skewness of a distribution.  
 
*Skewness is a measure of the degree of asymmetry of a distribution.  
 
*Skewness is a measure of the degree of asymmetry of a distribution.  
*A distribution(normal ditribution) is symmetry ,it don't have a skewness.  
+
*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.  
+
*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 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 mean<median<mode.
+
*In a left skewed distribution ,its <math>mean<median<mode</math>
*In a normal  skewed distribution, its mean=median=mode.
+
*In a normal  skewed distribution, its <math>mean=median=mode</math>
*In a right skewed distribution, its mode<median<mean.  
+
*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.  
 
*The arguments can be be either numbers or names, array,constants or references that contain numbers.  
 
*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.  
 
*Suppose the array contains text,logicl values or empty cells, like that values are not considered.  
*The equation for skewness is defined by :<math> Skewness = \frac{n}{(n-1)(n-2)}\sum \left(\frac{x_i-\bar{x}}{s} \right)^3</math> Where, s is the sample standard deviation, <math>\bar{x}</math> represents a sample mean.  
+
*The equation for Skewness is defined by :<math> Skewness = \frac{n}{(n-1)(n-2)}\sum \left(\frac{x_i-\bar{x}}{s} \right)^3</math>
 +
Where, <math>s</math> is the sample standard deviation, <math>\bar{x}</math> represents a sample mean.  
 
*This function will return the result as error when  
 
*This function will return the result as error when  
   1. Any one of the argument is nonnumeric.  
+
   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.
+
   2. If there are fewer than three data points, or the Sample Standard Deviation is zero.
  
 
==Examples==
 
==Examples==
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5.Array={1,2,3,5,6,11}
 
5.Array={1,2,3,5,6,11}
 
SKEW(A1:A6)=1.16584702768
 
SKEW(A1:A6)=1.16584702768
 
 
  
 
==See Also==
 
==See Also==

Revision as of 02:52, 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