LOGEST(known_ys,known_xs,affine,stat)
known_ys: known y-values
known_xs: known x-values; default to an array {1, 2, 3, …}
affine: if true, the model contains a constant term, defaults to true
stat: if true, extra statistical information will be returned; defaults to FALSE
LOGEST function applies the “least squares” method to fit an exponential curve of the form y = b * m{1}^x{1} * m{2}^x{2}... to your data.
LOGEST returns an array { m{n},m{n-1}, ...,m{1},b }.
Extra statistical information is written below the regression line coefficients in the result array. Extra statistical information consists of four rows of data. In the first row the standard error values for the coefficients m1, (m2, ...), b are represented. The second row contains the square of R and the standard error for the y estimate. The third row contains the F-observed value and the degrees of freedom. The last row contains the regression sum of squares and the residual sum of squares. If known_ys and known_xs have unequal number of data points, this function returns a #NUM! error.