LOGEST

Given partial data about an exponential growth curve, calculates various parameters about the best fit ideal exponential growth curve.

Sample Usage

LOGEST(B2:B10,A2:A10)

LOGEST(B2:B10, A2:A10, TRUE, TRUE)

Syntax

LOGEST(known_data_y, [known_data_x], [b], [verbose])

  • known_data_y - The array or range containing dependent (y) values that are already known, used to curve fit an ideal exponential growth curve.

    • If known_data_y is a two-dimensional array or range, known_data_x must have the same dimensions or be omitted.

    • If known_data_y is a one-dimensional array or range, known_data_x may represent multiple independent variables in a two-dimensional array or range. I.e. if known_data_y is a single row, each row in known_data_x is interpreted as a separated independent value, and analogously if known_data_y is a single column.

  • known_data_x - [ OPTIONAL - {1,2,3,...} with same length as known_data_y by default ] - The values of the independent variable(s) corresponding with known_data_y.

    • If known_data_y is a one-dimensional array or range, known_data_x may represent multiple independent variables in a two-dimensional array or range. I.e. if known_data_y is a single row, each row in known_data_x is interpreted as a separated independent value, and analogously if known_data_y is a single column.
  • b - [ OPTIONAL - TRUE by default ] - Given a general exponential form of y = b*m^x for a curve fit, calculates b if TRUE or forces b to be 1 and only calculates the m values if FALSE.

  • verbose - [ OPTIONAL - FALSE by default ] - A flag specifying whether to return additional regression statistics or only the calculated coefficient and exponents.

    • If verbose is TRUE, in addition to the set of exponents for each independent variable and the coefficient b, LOGEST returns

      • The standard error for each exponent and the coefficient,

      • The coefficient of determination (between 0 and 1, where 1 indicates perfect correlation),

      • Standard error for the dependent variable values,

      • The F statistic, or F-observed value indicating whether the observed relationship between dependent and independent variables is random rather than exponential,

      • The degrees of freedom, useful in looking up F statistic values in a reference table to estimate a confidence level,

      • The regression sum of squares, and

      • The residual sum of squares.

Notes

  • The statistics calculated by LOGEST are similar to LINEST but use the linear model ln y = x1 ln m1 + ... + xn ln mn + ln b for each independent variable x1 ... xn. Therefore additional statistics such as the standard error must be compared to the natural logarithms of the m and b values rather than the values themselves.

See Also

TREND: Given partial data about a linear trend, fits an ideal linear trend using the least squares method and/or predicts further values.

LINEST: Given partial data about a linear trend, calculates various parameters about the ideal linear trend using the least-squares method.

GROWTH: Given partial data about an exponential growth trend, fits an ideal exponential growth trend and/or predicts further values.

Examples

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