KURT.P OpenBI
How heavy the tails of a column's numbers are when they are all there is: the population excess kurtosis.
Excess kurtosis compares the tails with a normal distribution's, which scores 0: positive means more values far out than a bell curve would have, negative fewer (a flat, even spread).
Group: Aggregation. OpenBI’s own: DAX has no function of this name.
Syntax
KURT.P ( <ColumnName> )| Parameter | Type | Description |
|---|---|---|
ColumnName | Column | A column of numbers. |
Returns
Number. The population excess kurtosis of the column's values in the filter context; BLANK for fewer than two values, or when they are all equal.
Remarks
- The population excess kurtosis: n × s4 ÷ s2² − 3, where s2 and s4 add up the squared and fourth powers of the differences from the mean. Excel has no function for it; it is SciPy's
kurtosiswithbias=Trueand type 1 of R's e1071 package. - Blanks are left out. Fewer than two values, or values that are all the same, give BLANK.
- It never goes below −2: two values far apart are the flattest spread there is.
Compared with DAX
An OpenBI function: DAX has none like it, so a measure or query that uses it works in OpenBI only, not in Power BI or Analysis Services.
Excel has KURT (the sample's, KURT.S here) but no population kurtosis.
Examples
The prices per liter, both ways
EVALUATE
ROW (
"Population", KURT.P ( Products[Price per liter] ),
"Sample", KURT.S ( Products[Price per liter] )
)| Population | Sample |
|---|---|
| -1.47833910034602 | -2.08754325259515 |
The monthly liters
EVALUATE
ROW ( "Excess kurtosis", KURT.P ( Sales[Liters] ) )| Excess kurtosis |
|---|
| 3.97558594032197 |