lognormal distribution

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log·nor·mal dis·tri·bu·tion

if a variable y is such that x = log y, it is said to have a lognormal distribution; this is a skew distribution.
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This corresponds to the lognormal distribution with the median of 3 and the dispersion factor of 3 shifted by 1 unit on the right.
However, it should be noted that this nonlinearity is driven by the use of a lognormal distribution for human variability, an approximation whose uncertainty increases substantially at levels of incidence much below 1% (Crump et al.
For example, Emam and Al-Deek [15] tested the lognormal, gamma, Weibull, and exponential distributions for representing travel time data of a freeway from weekdays and found that a lognormal distribution was best-fitted.
In this study, the BP mode takes a lognormal distribution with GMD of 500 nm, TPN of 4.34 x [1.0.sup.11] #/[m.sup.3], and GSD of 1.32, and SP modes also take lognormal distributions whose parameters are listed in Table 3.
The parameter of the difference between two population variances for lognormal distributions is
In the "A Procedure for Estimating Parameters of Truncated Lognormal Distribution" section, we present our simple estimation procedure for finding the method of moments estimates of the lognormal severity distribution parameters under the truncation approach.
We characterized this uncertainty using statistical simulations from normal and lognormal distributions. We found that the distribution of the injury risk is skewed to the right, indicating that there are occasional tests as proxies for real situations that lead to a very large injury risk.
Here the lognormal distribution is used to check how the final result will change when distribution type is changed.
S = sample standard deviation U = Cholesky decomposition of the correlation matrix [bar.X] = daily sample mean for a given schedule and day type n = the number in the sample population r = vector of standard normal random numbers v = vector of correlated normal random numbers x = the schedule value for a given hour, day and schedule number [sigma] = scaling factor for lognormal distributions [mu] = location factor for lognormal distributions Subscripts i = hour j = day type k = schedule number REFERENCES
Moments and parameters of Lognormal distributions assigned to the random variable [[DELTA].sub.L,m] Sample A_240/5 B_480/9 n 58 30 [pcs] [mu] 15.37 25.73 [mm] [[sigma].sup.2] 0.46 1.25 [mm] CoV 0.030 0.048 [-] [lambda] 2.73 3.24 [-] [[zeta].sup.2] 0.03 0.048 [-] Table 4.
Tables 1 and 2 contain results of the estimations for the model in which RND is the mixture of two lognormal distributions. In the Table 1 there are mean values of the parameters of RND (a mixture of two lognormal distributions) for 21st of March 2014 for each month within the sample (7).
Even though Weibull and Lognormal distributions lack theoretic justification for their use in channel amplitude modeling, they provide excellent data fit in many cases [10],[11],[12],[13],[14],[15],[16].