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Lognormal distribution of a random variable If X is a random variable and Y=ln(X) is normally distributed, then X is said to be distributed lognormally. Similarly, if Y has a normal distribution, then the exponential function of Y will be having a lognormal distribution, i.e. X=exp(Y).

Example DataDescription1.2Standard deviation of ln(x)FormulaDescriptionResult=LOGNORM.DIST(A2,A3,A4,TRUE)Cumulative lognormal distribution at 4, using the arguments in A2:A4.0.0390836=LOGNORM.DIST(A2,A3,A4,FALSE)Probability lognormal distribution at 4, using the same arguments.0.01761762 more rows

normal distribution is a continuous distribution of random variable y whose natural logarithm is normally distributed. For example, if random variable y = exp { y } has lognormal distribution then x = log ( y ) has normal distribution.

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Description. [ m , v ] = lognstat( mu , sigma ) returns the mean and variance of the lognormal distribution with the distribution parameters mu (mean of logarithmic values) and sigma (standard deviation of logarithmic values).

Using the lognormal distribution in R We'll start with dlnorm() and rlnorm() : dlnorm(x, meanlog = m, sdlog = s) is the probability density function (PDF) that returns the probability of observing the value x when it's drawn from a lognormal distribution X such that mean(log(X)) = m and sd(log(X)) = s .

The lognormal distribution is used to describe load variables, whereas the normal distribution is used to describe resistance variables. However, a variable that is known as never taking on negative values is normally assigned a lognormal distribution rather than a normal distribution.