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A lognormal distribution is a continuous probability distribution of a random variable in which logarithm is normally distributed. Thus, if the random variable has a lognormal distribution, then has a normal distribution. Likewise, if has a normal distribution, then has a lognormal distribution.

One key difference between the two is that lognormal distributions contain only positive numbers, whereas normal distribution can contain negative values. Another key difference between the two is the shape of the graph. Normally distributed data forms a symmetric bell-shaped graph, as seen in the previous graphs.

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

In probability theory, a log-normal (or lognormal) distribution is a continuous probability distribution of a random variable whose logarithm is normally distributed. Thus, if the random variable X is log-normally distributed, then Y = ln(X) has a normal distribution.

A lognormal distribution is a continuous probability distribution of a random variable in which logarithm is normally distributed. Thus, if the random variable has a lognormal distribution, then has a normal distribution. Likewise, if has a normal distribution, then has a lognormal distribution.

The lognormal distribution differs from the normal distribution in several ways. A major difference is in its shape: the normal distribution is symmetrical, whereas the lognormal distribution is not. Because the values in a lognormal distribution are positive, they create a right-skewed curve.

The log-logistic distribution is the probability distribution of a random variable whose logarithm has a logistic distribution. It is similar in shape to the log-normal distribution but has heavier tails. Unlike the log-normal, its cumulative distribution function can be written in closed form.