# KURT

Calculates the kurtosis of a dataset, which describes the shape, and in particular the "peakedness" of that dataset.

### Sample Usage

`KURT(1,2,3,4,5,6,7,8,9,10)`

`KURT(A2:A100)`

### Syntax

`KURT(value1, [value2, ...])`

• `value1` - The first value or range of the dataset.

• `value2, ...` - Additional values or ranges to include in the dataset.

### Notes

• Although `KURT` is specified as taking a maximum of 30 arguments, Google Sheets supports an arbitrary number of arguments for this function.

• If the total number of values supplied as `value` arguments is not at least two, `KURT` will return the `#DIV/0!` error.

• Any text encountered in the `value` arguments will be ignored.

• Positive kurtosis indicates a more "peaked" distribution in the dataset, while negative kurtosis indicates a flatter distribution.

`VARPA`: Calculates the variance based on an entire population, setting text to the value `0`.

`VARP`: Calculates the variance based on an entire population.

`VARA`: Calculates the variance based on a sample, setting text to the value `0`.

`VAR`: Calculates the variance based on a sample.

`STDEVPA`: Calculates the standard deviation based on an entire population, setting text to the value `0`.

`STDEVP`: Calculates the standard deviation based on an entire population.

`STDEVA`: Calculates the standard deviation based on a sample, setting text to the value `0`.

`SKEW`: Calculates the skewness of a dataset, which describes the symmetry of that dataset about the mean.

`DVARP`: Returns the variance of an entire population selected from a database table-like array or range using a SQL-like query.

`DVAR`: Returns the variance of a population sample selected from a database table-like array or range using a SQL-like query.

`DSTDEVP`: Returns the standard deviation of an entire population selected from a database table-like array or range using a SQL-like query.

`DSTDEV`: Returns the standard deviation of a population sample selected from a database table-like array or range using a SQL-like query.

`DEVSQ`: Calculates the sum of squares of deviations based on a sample.

`AVEDEV`: Calculates the average of the magnitudes of deviations of data from a dataset's mean.