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Utilizing a normal distribution allows for uncertainty in the calibration estimates [ 87], which is important for our data because calibrations were taken from the host fossil record rather than from louse fossils.
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More specifically, a normal distribution with a specified standard deviation (default 20) is utilized.
For continuous data with a normal distribution, a one-way analysis of variance (ANOVA) was utilized for comparing the various settings and in those with significant differences.
When the variables did not have a normal distribution and ranked data, the Wilcoxon signed rank sum test was utilized.
The β values were utilized for RF, described below, which is a non-parametric method and does not assume a normal distribution.
Utilizing the central limit theorem we can prove that the ML estimates (hat {alpha }) and (hat {beta }) follow asymptotically a normal distribution, i.e., for a large sample size (|mathcal {D}|), the distribution of (hat {alpha }) and (hat {beta }) would be very close to the normal distribution.
Assuming a normal distribution for the uncertain variables, Latin Hypercube Sampling (LHS) method selects the sample values for simulation runs which are eventually utilized for calculating probability density function of constraints and their reliability at each design point.
That is a normal distribution.
The mean is a parameter of a normal distribution.
Because log x as a normal distribution had mean mu.
And one of the most universal random variable, our distribution is a normal distribution.
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