Difference between revisions of "Documentation/How Tos/Calc: TREND function"
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[[Image:Calc_trend_example.png|CENTER]] | [[Image:Calc_trend_example.png|CENTER]] | ||
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<tt>'''=TREND(B2:B5; A2:A5; A2:A7)'''</tt> | <tt>'''=TREND(B2:B5; A2:A5; A2:A7)'''</tt> | ||
: when entered as an array formula in cell C2, where the x values in A2:A7 are 1,2,3,4,5, 6 and the y values in B2:B4 are 2, 4, 6.1, 8 returns {2.01|4.02|6.03|8.04|10.05|12.06}. The data points are very nearly on the line y = 2x (and would be if B4 contained 6 instead of 6.1). The best fit line found is therefore very nearly y=2x. | : when entered as an array formula in cell C2, where the x values in A2:A7 are 1,2,3,4,5, 6 and the y values in B2:B4 are 2, 4, 6.1, 8 returns {2.01|4.02|6.03|8.04|10.05|12.06}. The data points are very nearly on the line y = 2x (and would be if B4 contained 6 instead of 6.1). The best fit line found is therefore very nearly y=2x. |
Revision as of 17:02, 29 January 2024
TREND
Fits a straight line to data using linear regression and returns points on that line.
Syntax:
TREND(yvalues; xvalues; new_xvalues; type)
- yvalues and xvalues are single row or column ranges specifying points in a set of data.
- TREND fits a straight line through these data points, using the linear regression method.
- If type is 0 the straight line found will pass through the origin; if type is non-zero or omitted the best fit straight line will be found.
- TREND returns an array of the y values of the straight line found, corresponding to the x values in new_xvalues (or if omitted xvalues). It must be entered as an array formula (for example by using Cntrl-Shift-Enter rather than just Enter).
- yvalues and xvalues must be the same size. new_xvalues may have a different size.
Example:
=TREND(B2:B5; A2:A5; A2:A7)
- when entered as an array formula in cell C2, where the x values in A2:A7 are 1,2,3,4,5, 6 and the y values in B2:B4 are 2, 4, 6.1, 8 returns {2.01|4.02|6.03|8.04|10.05|12.06}. The data points are very nearly on the line y = 2x (and would be if B4 contained 6 instead of 6.1). The best fit line found is therefore very nearly y=2x.
- This example shows how TREND may be used to predict future values.
See Also