Manuals/calci/STDEVP - Revision history

Devika at 22:19, 8 August 2018

2018-08-08T22:19:44Z

Devika at 21:21, 12 June 2018

2018-06-12T21:21:55Z

Devika at 20:30, 18 January 2018

2018-01-18T20:30:13Z

Jayaram at 07:20, 13 March 2017

2017-03-13T07:20:52Z

Jayaram at 11:34, 9 March 2017

2017-03-09T11:34:12Z

Swapna at 21:00, 25 June 2015

2015-06-25T21:00:03Z

Devika: /* References */

2015-05-11T18:18:17Z

References

Abin at 10:43, 30 January 2014

2014-01-30T10:43:59Z

Devika: /* Examples */

2014-01-25T09:19:54Z

Examples

Devika at 09:18, 25 January 2014

2014-01-25T09:18:34Z

Show changes

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 <div style="font-size:30px">'''STDEVP()'''</div><br/>
 *Parameters are set of numbers.
 ==Description==

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-<div style="font-size:30px">'''STDEVP(n1,n2,n3…)'''</div><br/>
+<div style="font-size:30px">'''STDEVP()'''</div><br/>
-*<math>n1,n2,n3... </math>  are numbers.
+*Parameters are set of numbers.
 ==Description==
@@ Line 7: / Line 7: @@
 *It is the  used as a measure of the dispersion or variation in a distribution.
 *It is calculated as the square root of variance.
-*In <math>STDEVP(n1,n2,n3...)</math>, <math>n1,n2,n3...</math> are numbers to find the Standard Deviation.
+*In <math>STDEVP()</math>, Parameters are set of numbers to find the Standard Deviation.
-*Here <math> n1 </math> is required. <math> n2,n3,...</math> are optional.
+*Here First Parameter is required. From the second parameter are optional.
 *Instead of numbers we can use the single array or a reference of a array.
 *<math> STDEVP </math> is defined by the formula:

@@ Line 13: / Line 13: @@
 <math>S.D= \sqrt \frac {\sum(x-\bar{x})^2}{(n-1)} </math>
 where <math> \bar{x} </math>  is the sample mean of x and n is the total numbers in the given data.
-*It is calculated using <math> "n" </math> method.
+*It is calculated using <math> n </math> method.
 *This function is considering our given data as the entire population.
 *Suppose it should consider the data as the sample of the population, we can use the [[Manuals/calci/STDEV  | STDEV ]] function.

← Older revision		Revision as of 07:20, 13 March 2017
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−	[[Z_API_Functions \| List of Main Z Functions]]	+	*[[Z_API_Functions \| List of Main Z Functions]]

−	[[ Z3 \| Z3 home ]]	+	*[[ Z3 \| Z3 home ]]

← Older revision		Revision as of 11:34, 9 March 2017
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	==References==		==References==
	*[http://en.wikipedia.org/wiki/Standard_deviation Standard Deviation]		*[http://en.wikipedia.org/wiki/Standard_deviation Standard Deviation]
		+
		+
		+
		+	[[Z_API_Functions \| List of Main Z Functions]]
		+
		+	[[ Z3 \| Z3 home ]]

@@ Line 45: / Line 45: @@
 #=STDEVP(A3:E3) = 44.58250778015
 #=STDEVP(0,2,8,10,11.7,23.8,32.1,43.7) = 14.389530699435
 ==See Also==

← Older revision		Revision as of 18:18, 11 May 2015
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	==References==		==References==
		+	*[http://en.wikipedia.org/wiki/Standard_deviation Standard Deviation]

@@ Line 1: / Line 1: @@
 <div style="font-size:30px">'''STDEVP(n1,n2,n3…)'''</div><br/>
 *<math>n1,n2,n3... </math>  are numbers.
 ==Description==
-*This function gives the standard deviation based on a entire population  as the  the given data .
+*This function gives the standard deviation based on a entire population as the the given data .
-*Standard deviation is a quantity expressing by how much the members of a group differ from the mean value for the group.
+*Standard Deviation is a quantity expressing by how much the members of a group differ from the mean value for the group.
 *It is the  used as a measure of the dispersion or variation in a distribution.
 *It is calculated as the square root of variance.
-*In <math>STDEVP(n1,n2,n3...), n1,n2,n3...,</math> are numbers to find the standard deviation.
+*In <math>STDEVP(n1,n2,n3...)</math>, <math>n1,n2,n3...</math> are numbers to find the Standard Deviation.
-*Here  <math> n1 </math> is required. <math> n2,n3,...</math> are optional.
+*Here <math> n1 </math> is required. <math> n2,n3,...</math> are optional.
 *Instead of numbers we can use the single array or a reference of a array.
 *<math> STDEVP </math> is defined by the formula:
-<math>S.D= \sqrt \frac {\sum(x-\bar{x})^2}{(n-1)} </math> where <math> \bar{x} </math>  is the sample mean of x and n is the total numbers in the given data.
+<math>S.D= \sqrt \frac {\sum(x-\bar{x})^2}{(n-1)} </math>
 *It is calculated using <math> "n" </math> method.
-*This function is considering our given data is the entire population.
+*This function is considering our given data as the entire population.
-*Suppose it should consider the data as the sample of the population, we can use the  [[Manuals/calci/STDEV  | STDEV ]] function.
+*Suppose it should consider the data as the sample of the population, we can use the [[Manuals/calci/STDEV  | STDEV ]] function.
 *For  huge sample sizes the functions <math> STDEV </math> and <math> STDEVP </math> are approximately equal values.
 *The arguments can be  either numbers or names, array,constants or references that contain numbers.
@@ Line 22: / Line 22: @@
 *Suppose the function have to consider the logical values and text representations of numbers in a reference , we can use the [[Manuals/calci/STDEVPA  | STDEVPA ]] function.
 *This function will return the result as error when
-. Any one of the argument is nonnumeric.
+. Any one of the argument is non-numeric.
 . The arguments containing the error values or text that cannot be translated in to numbers.
 ==Examples==
@@ Line 42: / Line 41: @@
 |}
-#STDEVP(A1:E1) = 149.0597195757
+#=STDEVP(A1:E1) = 149.0597195757
-#STDEVP(A2:G2) = 76.31463871127
+#=STDEVP(A2:G2) = 76.31463871127
-#STDEVP(A3:E3) = 44.58250778015
+#=STDEVP(A3:E3) = 44.58250778015
-#STDEVP(0,2,8,10,11.7,23.8,32.1,43.7) = 14.389530699435
+#=STDEVP(0,2,8,10,11.7,23.8,32.1,43.7) = 14.389530699435
 ==See Also==

@@ Line 33: / Line 33: @@
 |-
 ! 1
-| 87 || 121 || 427 ||390 || 110 || ||
+| 87 || 121 || 427 ||390 || 110 || 54 || 32
 |-
 ! 2
@@ Line 39: / Line 39: @@
 |-
 ! 3
-| 9  || -4  ||-29  ||38 || 101 || || ||
+| 9  || -4  ||-29  ||38 || 101 || 19 || 45
 |}
@@ Line 46: / Line 46: @@
 #STDEVP(A3:E3) = 44.58250778015
 #STDEVP(0,2,8,10,11.7,23.8,32.1,43.7) = 14.389530699435
 ==See Also==