Basic properties of lognormal distribution
A detailed discussion of the mathematical properties of lognormal distribution is found in this previous post in a companion blog. This post shows how to work basic calculation problems for lognormal distribution. A summary of lognormal distribution is given and is followed by several examples. Practice problems are in the next post.
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Basic Properties
The random variable is said to follow a lognormal distribution with parameters and if follows a normal distribution with mean and variance . Here, is the natural logarithm in base = 2.718281828…. It is difficult (if not impossible) to calculate probabilities by integrating the lognormal density function. Since the lognormal distribution is intimately related to the normal distribution, the basic lognormal calculation is performed by calculating the corresponding normal distribution. The following summary shows how.
In the following points, has a lognormal distribution with parameters and and is the corresponding normal distribution with mean and variance . The notation means raising to the number .
1. Lognormal observations and normal observations 

2. Lognormal CDF and normal CDF 

3. Lognormal density function and normal density function 

4. Lognormal moments and normal moment generating function 

5. Examples of lognormal moments 

6. Lognormal percentiles and normal percentiles 

7. Constant multiple of lognormal distribution 

8. Mode of lognormal distribution 

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Examples
Two examples are given to illustrate the calculation discussed here. The next post has practice problems.
All normal probabilities are obtained by using the normal distribution table found here.
Example 1
Suppose that the random variable has a lognormal distribution with parameters = 1 and = 2. Calculate the following.
 and
 The 67th, 95th and 99th percentiles of .
 Let . Find and
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To find the percentiles, first find the standard normal percentiles, either by using calculator or by looking up a table. Using a standard normal table, we get 0.44 (67th percentile), 1.645 (95th percentile) and 2.33 (99th percentile). The following gives the lognormal percentiles.

(67th percentile)
(95th percentile)
(99th percentile)
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The random variable has a lognormal distribution with parameters and = 2.
Note. One interpretation of is that of inflation, in this case a 10% inflation. For example, let be the size of a randomly selected auto insurance collision claim in the current year. If the claims are expected to increase 10% in the following year, is the the size of a randomly selected claim in the following year.
Example 2
Suppose that the random variable has a lognormal distribution with mean 12.18 and variance 255.02. Calculation the following.
 The skewness and kurtosis of .
First, determine the parameters and by setting up the following equations.
Plug the first equation into the second equation and obtain the equation . Solving for produces = 1. Plug = 1 into the first equation produces = 2. The following gives the desired probability.
To find the skewness and kurtosis, one way is to find the first 4 lognormal moments and then calculate the third standardized moment (skewness) and the fourth standardized moment (kurtosis). To see how this is done, see this previous post. Another is to use the formulas given above.
Example 3
Suppose that the lifetime (in years) of a certain type of machines follows the lognormal distribution described in Example 2. Suppose that you purchased such a machine that is 10year old. What is the probability that it will last another 10 years?
This is a conditional probability since the machine already survived for 10 years already.
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2017 – Dan Ma
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