Figure 2.

Determination of the linearity rejection region by Monte Carlo simulations. 3–150 samples were used from linear functions which were imposed by additional Gaussian noise. The example for m = 20 is shown, for which in some cases, fewer than 20 samples were returned due to outliers that were caused by the imposed technical error. For each of these mreturn values, adapted maximum likelihood limits were determined for which the null hypothesis, the existence of a linearity, would need to be rejected.

Kose et al. BMC Bioinformatics 2007 8:162   doi:10.1186/1471-2105-8-162
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