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Experimental Data Analyst—Quick Reference
Using available options, Experimental Data Analyst (EDA) is capable of very sophisticated analysis. This guide briefly reviews the use of common EDA routines for simple analysis tasks.
Data Formats
Dependent variable only:
Independent and dependent variable:
Explicit errors in the dependent variable:
Explicit errors in both coordinates:
Further information: §1.2
Importing and Exporting Data from/to an ASCII File
Further information: §2.1 and §2.2
Propagation of Errors of Precision
Using *WithError Functions
For data of the form:
then use:
More generally, if the data are related by:
then use:
Further information: §3.3.1
Using Datum and Data Constructs
For data of the form:
or:
then:
Further information: §3.3.1.1
Fitting to a Straight Line
Fit to:
with:
Further information: §4.2
Fitting to a Polynomial
Fit to:
with:
Further information: §4.2
Fitting to an Exponential
Fit to:
with:
Further information: §8.4.2
Fit to Gaussian Spectra
Fit to:
with:
Further information: §8.4.1
Fit to Lorentzian Spectra
Fit to:
with:
Further information: §8.4.1
Fit to a General Model
Fit to:
where x is the independent variable,  is the i-th parameter with initial value  with:
Further information: §5.1
Data Massage
To smooth out noise in a bivariate dataset:
Further information: §6.1.1, §6.1.2
To do a nonparametric fit with a "loess" technique using polynomials of order order:
Further information: §6.1.3
To fill missing data points in multivariate data:
Further information: §6.1.6
Robust Fit to a Line
Fit to:
using robust techniques with:
or:
Further information: §7.1.1 and §7.1.2
Robust Fitting to a Polynomial
Fit to:
using robust techniques with:
Further information: §7.1.2
Miscellaneous Graphics
Generalized ListPlot:
Further information: §8.1.1
Histogram:
Further information: §8.1.2
BoxPlot:
Further information: §8.1.3
Log plot:
Further information: §8.1.4
Log-log plot:
Further information: §8.1.4
Miscellaneous
To find peaks in a dataset:
Further information: §8.3
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