Experimental Data Analyst

From extensive data fitting capabilities to data visualization and transformation, Experimental Data Analyst provides you with an impressive new set of detailed programs and packages that help you get the most out of your experimental data. A wide array of examples that include real experimental data quickly gets you up and running on your own projects and helps you harness the power and flexibility of Mathematica.

Extensive error analysis capabilities in Experimental Data Analyst easily handle errors in both coordinates of the data, obtain estimated errors in the fit parameters, and examine graphical information about the fit, including residuals of the fit.

Experimental Data Analyst allows you to fit data to linear or arbitrary models. You can fit data to lines or curves when one or more of the data points may be "wild" and the least-squares technique cannot be used. For advanced problems, it's easy to customize the behavior of the fitting routines by selecting from numerous options. Or, for less complex cases, you can simply rely on the defaults for quick, accurate solutions.

A variety of data transformation techniques such as data smoothing and noise elimination are available, as well as routines that automatically propagate errors of precision.

Impressive graphics capabilities provide you with a rich environment for the visualization of your experimental data. An extension of Mathematica's ListPlot function visualizes errors in your data coordinates with error bars. The distribution of your data values can be viewed pictorially using histograms or box plots. You can fully control the display based on the data, the number of bins, the min, and the max.

Features

Data Fitting

Least-Square Linear Fitting with Arbitrary Basis Functions
Nonlinear Fitting Using the Levenberg-Marquardt Iterative Algorithm
Parameter Estimation in Spectra Peaks
Data Fitting with Outliers Using the Median or Weighted Residuals
Data Smoothing with Averaging Techniques, Fourier Filters, or Loess Fitting

Error Analysis

Error Estimation in the Fit Parameters
Errors in Both Coordinates of the Data
Value Adjustment for Significant Figures
Errors Combined in Quadrature

Data Visualization and Transformation

Histograms
Box Plots
Error Coordinates Automatically Displayed
ListPlot Extension

Reading and Using Data

ASCII or Binary Format Importing and Exporting
Real Experimental Data from a Variety of Fields

Stefan Steinhaus, webmaster@steinhaus-net.de