Creating Jackknife and Bootstrap estimates of the covariance matrix for
  the two-point correlation function
  We present correction terms that allow delete-one Jackknife and Bootstrap
methods to be used to recover unbiased estimates of the data covariance matrix
of the two-point correlation function $\xi\left(\mathbf{r}\right)$. We
demonstrate the accuracy and precision of this new method using a large set of
1000 QUIJOTE simulations that each cover a comoving volume of
$1\rm{\left[h^{-1}Gpc\right]^3}$. The corrected resampling techniques
accurately recover the correct amplitude and structure of the data covariance
matrix as represented by its principal components. Our corrections for the
internal resampling methods are shown to be robust against the intrinsic
clustering of the cosmological tracers both in real- and redshift space using
two snapshots at $z=0$ and $z=1$ that mimic two samples with significantly
different clustering. We also analyse two different slicing of the simulation
volume into $n_{\rm sv}=64$ or $125$ sub-samples and show that the main impact
of different $n_{\rm sv}$ is on the structure of the covariance matrix due to
the limited number of independent internal realisations that can be made given
a fixed $n_{\rm sv}$.
