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And, finally the required sample size per arm for a trial at pre-specified power 1 - β to detect a pre-specified difference of d, is n I, where: (3) Using Normal approximations, the above formulae can be used for binary outcomes, by approximating the variance of the proportions π1 and π2, by: (4) for testing the two sided hypothesis H0 : π1 = π2.
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When using the MCPC CAZAC waveforms to compute the measurement likelihoods in (11), we can reduce the computational complexity by approximating the above variance expressions.
Using (22) for approximating the variance, following equation is obtained.
(14) (15) When the measurement error models and PPD can reasonably be assumed Gaussian, one can approximate the variance reduction by approximating the PPD between the output and the measurements of interest with a multivariate Gaussian distribution.
The Rytov variance is obtained by approximating the solution to Maxwell's equations for electromagnetic wave propagation through a random medium under weak turbulence conditions.
By approximating the sample fluxes as normal distributions, we measured the sample mean and variance of the stochastic fluxes on sets of 25 simulations for the three cases of initial A levels, given in the Additional file 1.
These expressions are derived by approximating the Two-Degree-of-Freedom PID controller to the Two-Degree-of-Freedom generalized minimum variance control (GMVC) law.
In the texture-mapping procedure, we begin by approximating the target surface with an approximating cylinder.
In a biplot, the length of a line approximates the variance of the variable.
The empirical variance of those estimates approximates the variance of the original estimator.
Then we use OPLS-DA to decompose Z into a -correlated part and a -uncorrelated part by: (5) where B O P O approximates the -uncorrelated variance in Z and E is the residual.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com