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<< Click to Display Table of Contents >> FORECAST (forecast using linear regression) |
Syntax:
FORECAST(x, y_values, x_values)
Description:
Returns the y coordinate for the given x coordinate on a best-fit line based on the given values.
A best-fit line is the result of a linear regression, a statistical technique that adapts a line to a set of data points (for example, the results of a series of measurements).
The FORECAST function allows you to predict what value y (the dependent variable) will approximately have at a certain value x (the independent variable).
This function can be used to predict, for example, the resistance of a temperature-sensitive resistor at a specific temperature after having measured the resistance at several other temperatures.
x is the value x for which a prediction is desired.
For the y_values and x_values arguments, you usually specify a cell range.
y_values are the known y values (e.g., the resistance).
x_values are the known x values (e.g., the temperature).
Note:
Note that this function expects first the y_values and then the x_values as second and third arguments – not the other way around.
Annotation:
The linear regression is performed with this function using the least squares method.
Example:
The resistance of a temperature-sensitive resistor has been measured at several temperatures.
Cells A1:A4 contain the temperatures measured: 8, 20, 25, 28
Cells B1:B4 contain the resistances measured: 261, 508, 608, 680
The following calculation returns an estimate for the resistance at 15 degrees:
FORECAST(15, B1:B4, A1:A4) returns 405.21805 (Ohm)
Annotation:
INTERCEPT(y_values, x_values) equals FORECAST(0, y_values, x_values).
See also: