 Distribution Fitting Software & Articles

# Lognormal Distribution

EasyFit: select the best fitting distribution and use it to make better decisions. learn more The Lognormal distribution is based on the Normal distribution. A random variable is lognormally distributed if the logarithm of the random variable is normally distributed.

### Parameters - scale parameter of the included Normal distribution (  ) - location parameter of the included Normal distribution

### Domain ### Probability Density Function (PDF) ## Lognormal Distribution Fitting

EasyFit allows to automatically or manually fit the Lognormal distribution and 55 additional distributions to your data, compare the results, and select the best fitting model using the goodness of fit tests and interactive graphs. Watch the short video about EasyFit and get your free trial.

## Lognormal Distribution Graphs and Properties

EasyFit displays all graphs and properties of the Lognormal distribution, presenting the results in an easy to read & understand manner. EasyFit calculates statistical moments (mean, variance etc.), quantiles, tail probabilities depending on the distribution parameters you specify.

## Random Numbers from the Lognormal Distribution

You can easily generate random numbers from the Lognormal distribution in a variety of ways:

• directly from EasyFit
• in Excel sheets using the worksheet functions provided by EasyFitXL
• in your VBA applications using the EasyFitXL library

## Excel Worksheet and VBA Functions

EasyFitXL enables you to use the following functions in your Excel sheets and VBA applications:

Function Name
Description
`LognormalPdf` Probability Density Function
`LognormalCdf` Cumulative Distribution Function
`LognormalHaz` Hazard Function
`LognormalInv` Inverse CDF (Quantile Function)
`LognormalRand` Random Numbers
`LognormalMean` Mean
`LognormalVar` Variance
`LognormalStdev` Standard Deviation

Learn more: EasyFit Help on the Lognormal distribution

## Applications

The Lognormal distribution has widespread application. It is extensively used for reliability analysis. It can also be used to model:

• the long-term return rate on a stock investment;
• the weight and blood pressure of humans;
• the survival time of bacteria in disinfectants.