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Most statistical analyses rely on distributional assumptions for observed data (e.g. Normal distribution for continuous outcomes, Poisson distribution for count data, or binomial distribution for binary outcome data).
Logarithm transformation was used to assume a normal distribution for continuous variables.
The Kolmogorov-Smirnov test was used to test the normality of distribution for continuous variables.
The data were expressed as mean ± SD (for normal distribution) and median (for non-normal distribution) for continuous variables and as percentage (number) for categorical variables.
Baseline characteristics will be presented for each randomised group as the mean±SD or the median (IQR) according to the statistical distribution for continuous data, and as the number of patients and associated percentages for categorical parameters.
Baseline characteristics will be presented for each randomised group as the mean±standard-deviation or the median (IQR) according to the statistical distribution for continuous data, and as the number of patients and associated percentages for categorical parameters.
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Graph diffusion kernels are solutions to the steady-state density distribution for continuous-time random walk or diffusive process on a graph with sources and sinks [ 8].
We represent deterministic conditional distributions for continuous variables using Dirac delta functions.
It will be demonstrated that the developed analytical approach is capable to predict such moisture distributions for continuous drying processes.
317 (2002) 753 764; U. Nodelman, C.R. Shelton, D. Koller, Expectation maximization and complex duration distributions for continuous time Bayesian networks, in: Proceedings of the twenty-first conference on uncertainty in AI (UAI), 2005, pp. 421 430; M. Bladt, M. Sørensen, Statistical inference for discretely observed Markov jump processes, J.R. Statist.
Natural logarithm transformation was used to produce near-normal distributions for continuous TGF-beta1 (lnTGF-beta1).
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