Experimental Data Analyst 1.3
Fitting, Visualization, and Error
Analysis with the Power
From extensive data fitting capabilities to data visualization and
transformation, Experimental Data Analyst provides you with an
impressive 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
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. For less complex cases, you can simply rely on the defaults
for quick, accurate solutions.
Various data transformation techniques such as data smoothing and
noise elimination as well as routines that automatically propagate
errors of precision are available.
Impressive graphics capabilities provide a rich environment
for visualizing your experimental data. An extension
of Mathematica's function ListPlot 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.
The package comes with electronic documentation, which is fully
integrated with the Mathematica Help Browser.
Experimental Data Analyst is developed and supported by the University of Toronto Department of Physics.
University of Toronto
Department of Physics
60 St. George Street
Toronto, ON M5S 1A7
Experimental Data Analyst 1.3 requires Mathematica 6, 7, 8, or 9 and is available for Windows, Mac OS X, and Linux.