# LOGINV

Returns the value of the inverse log-normal cumulative distribution with given mean and standard deviation at a specified value.

### Sample Usage

`LOGINV(0.4,4,6)`

`LOGINV(A2,A3,A4)`

### Syntax

`LOGINV(x, mean, standard_deviation)`

• `x` - The input to the inverse log-normal cumulative distribution function.

• `mean` - The mean (mu) of the inverse log-normal cumulative distribution function.

• `standard_deviation` - The standard deviation (sigma) of the inverse log-normal cumulative distribution function.

### See Also

`WEIBULL`: Returns the value of the Weibull distribution function (or Weibull cumulative distribution function) for a specified shape and scale.

`POISSON`: Returns the value of the Poisson distribution function (or Poisson cumulative distribution function) for a specified value and mean.

`NORMSINV`: Returns the value of the inverse standard normal distribution function for a specified value.

`NORMSDIST`: Returns the value of the standard normal cumulative distribution function for a specified value.

`NORMINV`: Returns the value of the inverse normal distribution function for a specified value, mean, and standard deviation.

`NORMDIST`: The NORMDIST function returns the value of the normal distribution function (or normal cumulative distribution function) for a specified value, mean, and standard deviation.

`NEGBINOMDIST`: Calculates the probability of drawing a certain number of failures before a certain number of successes given a probability of success in independent trials.

`LOGNORMDIST`: Returns the value of the log-normal cumulative distribution with given mean and standard deviation at a specified value.

`EXPONDIST`: Returns the value of the exponential distribution function with a specified lambda at a specified value.

`BINOMDIST`: Calculates the probability of drawing a certain number of successes (or a maximum number of successes) in a certain number of tries given a population of a certain size containing a certain number of successes, with replacement of draws.

### Examples

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