Calculates the exponential regression fit curve that best matches the data and returns a function that describes the array of values for that curve.
grammar
LOGEST(known_y’s, [known_x’s], [const], [stats])
The LOGEST function syntax has the following parameters:
■known_y’s required. The set of known y values in the relation expression y = b*m^x.
If the array known_y’s is in a separate column, then each column of known_x’s is treated as an independent variable.
If the array known_y’s is in a separate row, then each row of known_x’s is treated as an independent variable.
■known_x’s Optional. The set of known x values in the relation expression y=b*m^x, which is an optional argument.
The array known_x’s can contain one or more sets of variables. If only one variable is used, as long as known_x’s and known_y’s have phases
With the same dimension, they can be regions of any shape.
If more than one variable is used, known_y’s must be a vector (that is, a region of cells with a column height or a row width).
If known_x’s is omitted, the array is assumed to be {1,2,3,… }, which is the same size as known_y’s.
■ Constant optional. A logical value that specifies whether to force the constant b to be 1.
If const is TRUE or omitted, b will be calculated as normal.
If const is FALSE, then the constant b is set to 1, and the value of m satisfies the formula y=m ^ x.
■ Cut-off optional. A logical value that specifies whether to return an additional regression statistic.
If stats is TRUE, the function LOGEST returns additional regression statistics,
So the array returned is {mn,mn-1,… ,m1,b; sen,sen-1,… ,se1,seb; r 2,sey; F,df; ssreg,ssresid}.
If stats is FALSE or omitted, the function LOGEST returns only the coefficients m and the constant b.
Instructions
The more the graph drawn from the data approximates an exponential curve, the more the calculated curve conforms to the original given data.
■ Like the LINEST function, the LOGEST function returns an array of values that describe the relationships between values,
But the LINEST function fits the data with a straight line, while the LOGEST function fits the data with an exponential curve.
■ When there is only one independent variable x, the value of y-intercept (b) can be calculated directly using the following formula:
INDEX(LOGEST(known_y’s,known_x’s),2)
The value of y can be predicted by the formula y=b*m ^ x
■ When entering an array constant (such as known_x’s) as an argument, use commas to separate the values in the same row and semicolons to separate the lines.
The separator may vary depending on the locale.
■ Note that if the y value predicted by the regression formula is outside the range of y values used to calculate the regression formula, the value may not be valid.
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