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The mentioned procedures are valid under the assumption that the paired laterals are laid on straight slopes; however, real microirrigation units rarely follow an even gradient, whose topography is characterized by equally spaced contour lines.
However, this test is only valid under the assumption of valid instruments.
A similar conclusion is valid under the assumption that the initial distribution of population, (f(a)), undergo small perturbations.
The last step of calculation is valid under the assumption of small time and frequency offsets, i.e., a i =1.
Conformal predictors are always valid under the assumption of exchangeability, i.e., that predicted compounds are drawn from the same distribution as compounds used to develop the prediction model.
This approach is therefore valid under the assumption that there are no tip or sample changes due to tip-sample interaction.
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Most importantly, these hypotheses are only valid under the assumptions that no other components affect the complex lifetime and that the receptor usage of a ligand in vivo solely depends on the receptor affinity, that is, the receptor with the highest binding affinity is the receptor to be recruited by this ligand.
The imputation procedure implicit in the IPWRA method is valid only under the assumption that assignment to treatment is independent on potential outcomes, conditional on a set of predetermined characteristics X (the so called Conditional Independence Assumption CIA).
Although the analysis of Berger-Sabbatel et al. [4, 22] is rather consistent with the behavior of the present 802.11 protocols, however, the conclusion is valid only under the assumption of homogeneous transmission qualities among the participating hosts, which may be unrealistic while hosts can experience unequal channel conditions due to mobility, fading, interference factors, and so on.
We note here that the P value of the t-test is strictly valid only under the assumption of a normal distribution of the random variable considered, which in the present case is a good approximation.
The weighted analyses provide valid results under the assumption that the data are missing at random and that the observed variables modelled to drive non-response were correctly identified.
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