Manuals/calci/SLOPE - Revision history

Devika at 22:12, 1 August 2018

2018-08-01T22:12:27Z

Devika at 22:08, 1 August 2018

2018-08-01T22:08:45Z

Jayaram at 11:12, 14 March 2017

2017-03-14T11:12:41Z

Devika: /* Examples */

2017-01-12T19:58:23Z

Examples

Swapna at 20:21, 26 June 2015

2015-06-26T20:21:17Z

Devika at 17:40, 11 May 2015

2015-05-11T17:40:14Z

Abin at 09:55, 30 January 2014

2014-01-30T09:55:38Z

Abin: /* Examples */

2014-01-24T04:36:42Z

Examples

Abin: /* Examples */

2014-01-22T05:08:05Z

Examples

Abin: /* Examples */

2014-01-22T05:06:28Z

Examples

@@ Line 2: / Line 2: @@
 *<math> KnownYArray </math> is the set of dependent values.
 *<math> KnownXArray </math> is the set of independent  values.
 ==Description==

@@ Line 1: / Line 1: @@
-<div style="font-size:30px">'''SLOPE(y,x)'''</div><br/>
+<div style="font-size:30px">'''SLOPE (KnownYArray,KnownXArray)'''</div><br/>
-*<math> y </math> is the set of dependent values.
+*<math> KnownYArray </math> is the set of dependent values.
-*<math> x </math> is the set of independent  values.
+*<math> KnownXArray </math> is the set of independent  values.
 ==Description==
@@ Line 8: / Line 8: @@
 *To find a slope we can use the least squares method.
 *Slope is  found by calculating b as the co-variance of <math>x</math> and <math>y</math>, divided by the sum of squares (variance) of <math>x</math>.
-*In <math>SLOPE(y,x</math>), <math>y </math> is the array of the numeric dependent values and <math> x </math> is the array of the independent values.
+*In <math>SLOPE (KnownYArray,KnownXArray)</math>, <math>KnownYArray </math> is the array of the numeric dependent values and <math> KnownXArray </math> is the array of the independent values.
 *The arguments can be be either numbers or names, array, constants or references that contain numbers.
 *Suppose the array contains text, logical values or empty cells, like that values are not considered.
@@ Line 16: / Line 16: @@
 *This function will return the result as error when
 . Any one of the argument is non-numeric.
-. <math>x</math> and <math>y</math> are empty or that have a different number of data points.
+. <math>KnownYArray</math> and <math>KnownXArray</math> are empty or that have a different number of data points.
 ==Examples==

← Older revision		Revision as of 11:12, 14 March 2017
Line 72:		Line 72:
	==References==		==References==
	*[http://stattrek.com/regression/slope-test.aspx?Tutorial=AP Linear regression line]		*[http://stattrek.com/regression/slope-test.aspx?Tutorial=AP Linear regression line]
		+
		+
		+
		+	*[[Z_API_Functions \| List of Main Z Functions]]
		+
		+	*[[ Z3 \| Z3 home ]]

@@ Line 31: / Line 31: @@
 | 1 || 5 || 10 || 3 || 4
 |}
-  =SLOPE(A1:E1,B2:E2) = -0.305309734513
+  =SLOPE(A1:E1,A2:E2) = -0.305309734513
 .
 {| class="wikitable"

@@ Line 60: / Line 60: @@
 |}
   =SLOPE(C1:C3) = 0.730769230769
 ==See Also==

← Older revision		Revision as of 17:40, 11 May 2015
Line 67:		Line 67:

	==References==		==References==
		+	*[http://stattrek.com/regression/slope-test.aspx?Tutorial=AP Linear regression line]

@@ Line 2: / Line 2: @@
 *<math> y </math> is the set of dependent values.
 *<math> x </math> is the set of independent  values.
 ==Description==
@@ Line 8: / Line 7: @@
 *The slope of a regression line (b) represents the rate of change in <math> y </math> as ,math> x </math> changes.
 *To find a slope we can use the least squares method.
-*Slope is  found by calculating b as the covariance of x and y, divided by the sum of squares (variance) of x.
+*Slope is  found by calculating b as the co-variance of <math>x</math> and <math>y</math>, divided by the sum of squares (variance) of <math>x</math>.
-*In <math>SLOPE(y,x), y </math> is the array of the numeric dependent values and <math> x </math> is the array of the independent values.
+*In <math>SLOPE(y,x</math>), <math>y </math> is the array of the numeric dependent values and <math> x </math> is the array of the independent values.
-*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,logical values or empty cells, like that values are not considered.
+*Suppose the array contains text, logical values or empty cells, like that values are not considered.
-*The equation for the slope of the regression line is :<math>b = \frac {\sum (x-\bar{x})(y-\bar{y})} {\sum(x-\bar{x})^2}</math>.  where <math>\bar{x}</math> and <math>\bar{y}</math> are the sample mean x and y.
+*The equation for the slope of the regression line is
 *This function will return the result as error when
-. Any one of the argument is nonnumeric.
+. Any one of the argument is non-numeric.
-. x and y are empty or that have a different number of data points.
+. <math>x</math> and <math>y</math> are empty or that have a different number of data points.
 ==Examples==
@@ Line 64: / Line 65: @@
 *[[Manuals/calci/RSQ  | RSQ ]]
 *[[Manuals/calci/PEARSON | PEARSON ]]
 ==References==

@@ Line 44: / Line 44: @@
 |}
-  =SLOPE(A1:F1,A2:F2)=0.58510638297
+  =SLOPE(A1:F1,A2:F2) = 0.58510638297
 .
@@ Line 58: / Line 58: @@
 | -1 || 5 || 7
 |}
-  =SLOPE(C1:C3)=0.730769230769
+  =SLOPE(C1:C3) = 0.730769230769
 ==See Also==

@@ Line 18: / Line 18: @@
 ==Examples==
 .
-.{| class="wikitable"
+{| class="wikitable"
 |+Spreadsheet
 |-
@@ Line 44: / Line 44: @@
 |}
-*=SLOPE(A1:F1,A2:F2)=0.58510638297
+ =SLOPE(A1:F1,A2:F2)=0.58510638297
 .y={0,9,4}