Difference between revisions of "Documentation/How Tos/Calc: STEYX function"

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== STEYX ==
 
== STEYX ==
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: where the x values in A2:A6 are 1, 2, 3, 4, 5 and the y values in B2:B6 are 2, 4, 7, 8, 10 returns approximately <tt>'''0.51'''</tt>. The equation of the straight line found is very nearly <i>y</i> = 2<i>x</i>, and would be if B4 contained 6. The standard error is therefore quite small.
 
: where the x values in A2:A6 are 1, 2, 3, 4, 5 and the y values in B2:B6 are 2, 4, 7, 8, 10 returns approximately <tt>'''0.51'''</tt>. The equation of the straight line found is very nearly <i>y</i> = 2<i>x</i>, and would be if B4 contained 6. The standard error is therefore quite small.
  
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* [[Documentation/How_Tos/Calc: FORECAST function|FORECAST]]
 
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* [[Documentation/How_Tos/Calc: Functions listed alphabetically|Functions listed alphabetically]]
 
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Latest revision as of 14:47, 2 February 2024



STEYX

Fits a straight line to data using linear regression and returns the standard error of y values.

Syntax:

STEYX(yvalues; xvalues)

yvalues and xvalues are single row or column ranges specifying points in a set of data. yvalues and xvalues must be the same size.
STEYX in effect fits a straight line through these data points, using the linear regression method (least squares). It then returns the standard error of actual y values compared to y values on the straight line found.
The equation of a straight line may be given as y = a + bx. The linear regression method calculates:
Calc linregress b.png
and
Calc linregress a.png.
STEYX calculates:
Calc steyx equation.png
where yi are the actual values and yi' are the computed values on the line.


Advanced topic:
The parameters xvalues and yvalues are always evaluated as array formulas.

Example:

STEYX(B2:B6; A2:A6)

where the x values in A2:A6 are 1, 2, 3, 4, 5 and the y values in B2:B6 are 2, 4, 7, 8, 10 returns approximately 0.51. The equation of the straight line found is very nearly y = 2x, and would be if B4 contained 6. The standard error is therefore quite small.


See Also
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