Difference between revisions of "Manuals/calci/SLOPE"
Jump to navigation
Jump to search
(Created page with "<div id="6SpaceContent" class="zcontent" align="left"> <font face="Arial, sans-serif"><font size="2">'''SLOPE'''</font></font><font face="Arial, sans-serif"><font size="2">...") |
|||
| Line 1: | Line 1: | ||
| − | <div | + | <div style="font-size:30px">'''SLOPE(y,x)'''</div><br/> |
| + | *<math> y </math> is the set of dependent values. | ||
| + | *<math> x </math> is the set of independent values. | ||
| − | |||
| − | + | ==Description== | |
| + | *This function gives the slope of the linear regression line through a set of given points. | ||
| + | *The slope of a regression line (b) represents the rate of change in y as x 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. | ||
| + | *In SLOPE(y,x), y is the array of the numeric dependent values and x 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. | ||
| + | *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. | ||
| + | *This function will return the result as error when | ||
| + | 1. Any one of the argument is nonnumeric. | ||
| + | 2. x and y are empty or that have a different number of data points. | ||
| − | + | ==Examples== | |
| − | + | 1.y={4,9,2,6,7} | |
| − | + | x={1,5,10,3,4} | |
| − | - | + | SLOPE(A1:A5,B1:B5)=-0.305309734513 |
| − | + | 2.y={2,9,3,8,10,17} | |
| + | x={4,5,11,7,15,12} | ||
| + | SLOPE(B1:B6,C1:C6)=0.58510638297 | ||
| + | 3.y={0,9,4} | ||
| + | x={-1,5,7} | ||
| + | SLOPE(C1:C3)=0.730769230769 | ||
| − | |||
| − | + | ==See Also== | |
| + | *[[Manuals/calci/INTERCEPT | INTERCEPT]] | ||
| + | *[[Manuals/calci/RSQ | RSQ ]] | ||
| + | *[[Manuals/calci/PEARSON | PEARSON ]] | ||
| − | |||
| − | |||
| − | |||
| − | + | ==References== | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
Revision as of 04:11, 20 January 2014
SLOPE(y,x)
- is the set of dependent values.
- is the set of independent values.
is the set of dependent values.
is the set of independent values.
Description
- This function gives the slope of the linear regression line through a set of given points.
- The slope of a regression line (b) represents the rate of change in y as x 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.
- In SLOPE(y,x), y is the array of the numeric dependent values and x 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.
- The equation for the slope of the regression line is :Failed to parse (syntax error): {\displaystyle b = \frac {\sum (x-\bar{x})(y-\bar{y})} {\sum(x-\bar{x})^2} . where and are the sample mean x and y.
- This function will return the result as error when
and 
are the sample mean x and y.1. Any one of the argument is nonnumeric. 2. x and y are empty or that have a different number of data points.
Examples
1.y={4,9,2,6,7}
x={1,5,10,3,4}
SLOPE(A1:A5,B1:B5)=-0.305309734513 2.y={2,9,3,8,10,17}
x={4,5,11,7,15,12}
SLOPE(B1:B6,C1:C6)=0.58510638297 3.y={0,9,4}
x={-1,5,7}
SLOPE(C1:C3)=0.730769230769
See Also