The LogarithmicFit command fits a logarithmic function of the form $y=a+b\ln \left(x\right)$ to data by performing a least-squares fit.  Given k data points, where each point is a pair of numerical values for ($x$, $y$), the LogarithmicFit command finds a and b such that the sum of the k residuals squared is minimized.  The ith residual is the value $y-a-b\ln \left(x\right)$ at the ith data point.

In the first calling sequence, the first parameter X is a Vector containing the k values of the independent variable x, and the second parameter Y is a Vector containing the k values of the dependent variable y.  The entries of X must evaluate to positive numbers.  In the second calling sequence, the first parameter XY is a Matrix with two columns, where the first column corresponds to X and the second column to Y. For X, Y, and XY, one can also use lists or Arrays; for details, see the Input Forms help page.

If the optional parameter v is provided, then the LogarithmicFit command returns the logarithmic function in variable v with the computed values of a and b.  Otherwise, a Vector containing values of a and b is returned.

The LogarithmicFit command calls the Statistics[LinearFit] command to fit the given data to the model.  Additional options accepted by the LinearFit command, such as weights=W where W is a Vector of weights, may be provided to LogarithmicFit. More information about the underlying linear regression solver is available on the LinearFit help page.

The options argument can contain one or more of the options shown below.  These options are described in more detail on the Statistics/Regression/Options help page.

output = name or string -- Specify the form of the solution.  The output option can take as a value the name solutionmodule, or one of the following names (or a list of these names): AtkinsonTstatistic, confidenceintervals, CookDstatistic, degreesoffreedom, externallystandardizedresiduals, internallystandardizedresiduals, leastsquaresfunction, leverages, parametervalues, parametervector, residuals, residualmeansquare, residualstandarddeviation, residualsumofsquares, rsquared, rsquaredadjusted, standarderrors, tprobability, tvalue, variancecovariancematrix. For more information, see the Statistics/Regression/Solution help page.

summarize = identical( true, false, embed ) -- Display a summary of the regression model

svdtolerance = realcons(nonnegative) -- Set the tolerance that determines whether a singular-value decomposition is performed.

weights = Vector -- Provide weights for the data points.

$\mathrm{with}\left(\mathrm{Statistics}\right)\:$

$X≔\mathrm{Vector}\left(\left[10\,30\,60\,100\,200\,352\right]\,\mathrm{datatype}=\mathrm{float}\right)\:$

$Y≔\mathrm{Vector}\left(\left[18.4\,26.1\,31.0\,34.5\,39.4\,43.3\right]\,\mathrm{datatype}=\mathrm{float}\right)\:$

$\mathrm{LogarithmicFit}\left(X\,Y\,v\right)$

$2.30938538792688+6.99557865847690\ln \left(v\right)$

$\mathrm{ls}≔\mathrm{LogarithmicFit}\left(X\,Y\,v\,\mathrm{summarize}=\mathrm{embed}\right)\:$

$\mathrm{ls}$

$2.30938538792688+6.99557865847690\ln \left(v\right)$

$W≔\mathrm{Vector}\left(\left[1\,2\,1\,1\,1\,3\right]\,\mathrm{datatype}=\mathrm{float}\right)\:$

$\mathrm{LogarithmicFit}\left(X\,Y\,\mathrm{weights}=W\right)$

The XY parameter was introduced in Maple 15.

For more information on Maple 15 changes, see Updates in Maple 15.

The Statistics[LogarithmicFit] command was updated in Maple 2016.

The summarize option was introduced in Maple 2016.

For more information on Maple 2016 changes, see Updates in Maple 2016.