Difference between revisions of "Manuals/calci/SLOPE"
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− | =SLOPE(A1:F1,A2:F2)=0.58510638297 | + | =SLOPE(A1:F1,A2:F2) = 0.58510638297 |
3. | 3. | ||
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| -1 || 5 || 7 | | -1 || 5 || 7 | ||
|} | |} | ||
− | =SLOPE(C1:C3)=0.730769230769 | + | =SLOPE(C1:C3) = 0.730769230769 |
==See Also== | ==See Also== |
Revision as of 23:36, 23 January 2014
SLOPE(y,x)
- 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 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.
- In is the array of the numeric dependent values and 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 :. where and 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.
A | B | C | D | E | |
---|---|---|---|---|---|
1 | 4 | 9 | 2 | 6 | 7 |
2 | 1 | 5 | 10 | 3 | 4 |
=SLOPE(A1:E1,B2:E2) = -0.305309734513
2.
A | B | C | D | E | F | |
---|---|---|---|---|---|---|
1 | 2 | 9 | 3 | 8 | 10 | 17 |
2 | 4 | 5 | 11 | 7 | 15 | 12 |
=SLOPE(A1:F1,A2:F2) = 0.58510638297
3.
A | B | C | |
---|---|---|---|
1 | 0 | 9 | 4 |
2 | -1 | 5 | 7 |
=SLOPE(C1:C3) = 0.730769230769
See Also