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Non-linear least squares regression was initially used to fit
some distributions of log2-transformed  data to a range of
symmetrical ¡®bell-shaped¡¯ theoretical population distributions.
This technique was also applied to theoretical cumulative
distributions of the same data. Regression was conducted in
Systat software v.10.2 (Systat, Richmond, CA, USA). Nonlinear
least squares regression was then employed to fit
cumulative log2-transformed  distributions to the normal
cumulative function (ZCF). Three parameters were estimated,
the mean and standard deviation (both log2), and the total
number (N) in the presumed unimodal distribution. N was
estimated rather than taken as a constant in the regression,
because of the desire to fit the data to the distribution without
assuming that N truly contained only wild-type isolates.
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  • 2014-09-25 10:54:54, 149.37 K

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dingd

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feixiaolin

ÈÙÓþ°æÖ÷ (ÎÄ̳¾«Ó¢)

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3Â¥2014-09-25 16:22:13
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