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>LOGEST</A
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>Name</H2
>LOGEST&nbsp;--&nbsp;</DIV
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>Synopsis</H2
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>LOGEST(known_y's[,known_x's,const,stat])</PRE
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>The 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.</P
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>If @known_x's is omitted, an array {1, 2, 3, ...} is used. LOGEST returns an array { m{n},m{n-1}, ...,m{1},b }.</P
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>If @known_y's and @known_x's have unequal number of data points, LOGEST returns #NUM! error.</P
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>If @const is FALSE, the line will be forced to go through (0,1),i.e., b will be one.  The default is TRUE.</P
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>If @stat is TRUE, extra statistical information will be returned. Extra statistical information is written bellow 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. </P
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>The default of @stat is FALSE.</P
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>See also</H2
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>        <A
HREF="r8332.html#GNUMERIC-LOGEST"
>LOGEST</A
>
,         <A
HREF="r4716.html#GNUMERIC-GROWTH"
>GROWTH</A
>
,         <A
HREF="r9528.html#GNUMERIC-TREND"
>TREND</A
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