The basic fit command is called `fit`. This command performs a minimization
using the Levenberg-Marquardt algorithm. `fit` can take two
arguments, those being 1) the number of iterations to be performed before the
user is asked whether to continue; and 2) the change in fit statistic
that defines convergence. The fit statistics available within XSPEC are the
and C statistics: the `statistic` command specifies which
one is to be used. The `bayes` command sets up Bayesian inference.
Other fit-minimization algorithms are available, and
can be selected using the `method` command. A genetic algorithm is
included and its operations controlled by the `genetic` command.
For non-background-subtracted data the `goodness` command does a Monte
Carlo calculation of the goodness-of-fit. The weighting algorithm
used to calculate
can be altered by the `weight` command. A
systematic model uncertainty can be included using the the
`systematic` command. If the CERN library is linked in then the
command `improve` can be used to try to check whether the minimum
found is local or global.

The `error` or `uncertain` command calculates error bounds for one
interesting parameter for the specified parameters and confidence
levels. To produce multi-dimensional errors the `steppar` command
is used to generate a fit-statistic grid. Two-dimensional grids may be
expressed as contour plots (using `plot contour`). The model
normalization can be set using the `renorm` command. The
normalization of the correction file background can be set with
`cornorm`, and can be set to minimize the fit statistic with
`recornorm`. The gain of the response matrix can be adjusted
using the `gain` command, which includes an option to fit for the
gain. `ftest` provides calculation of F-test values and probabilities.