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Statistics[LogarithmicFit] - fit a logarithmic function to data

Calling Sequence

LogarithmicFit(X, Y, v, options)

LogarithmicFit(XY, v, options)




Vector; values of independent variable



Vector; values of dependent variable



Matrix; values of independent and dependent variables



name; (optional) independent variable name



(optional) equation(s) of the form option=value where option is one of output or weights; specify options for the LogarithmicFit command



The LogarithmicFit command fits a logarithmic function of the form y=a+blnx 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 yablnx 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, standarderrors, variancecovariancematrix. For more information, see the Statistics/Regression/Solution help page.


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


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



Fit a logarithmic function to the provided data.






Use the weights option to assign a weight to each data point.  Because the v parameter is not provided, a Vector containing the computed model parameters is returned.





See Also

CurveFitting, Statistics, Statistics/Regression, Statistics/Regression/InputForms, Statistics/Regression/Options, Statistics/Regression/Solution, Statistics[LinearFit]

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