The kurtosis function determines the peakedness or flatness of the distribution curve defined by a set of data points. A normal distribution has a kurtosis of 0. A negative kurtosis value describes a distribution that is flatter than normal (platykurtic); a positive kurtosis value describes a distribution more peaked than normal (leptokurtic).


The kurtosis function is used to compute the peakedness or flatness of the distribution curve defined by a set of data points.



Expression: (kurtosis num1 num2 ...)

Arguments Name Type Description
Argument:num1 ... Number Any number of values


Returns a number corresponding to the the peakedness or flatness of the distribution curve defined by a set of data points. Any arguments that cannot be evaluated as numbers are ignored in the calculation of kurtosis. Also, if there are less than four data points or if the sample standard deviation is zero, the kurtosis function returns an error value.



Here are a number of links to Lambda coding examples which contain this instruction in various use cases.


Argument Types

Here are the links to the data types of the function arguments.

Number Integer

Here are also a number of links to functions having arguments with any of these data types.

++ += + /=
/ * -- -=
- abs acos add1
addMethod addi argument arithmetic
asin atan avg badd
balance bdiv binaryInsert binaryNand
binaryNor binaryNot binaryNxor binarySearch
bitwiseAnd bitwiseNand bitwiseNor bitwiseNot
bitwiseNxor bitwiseOr bitwiseShiftLeft bitwiseShiftRight
bitwiseXor bmod bmul boolean
cadd cdiv char character
cmod cmul code compareEQ
compareGE compareGT compareLE compareLT
compareNE compare cons cos
cosh count csub date
day days360 debugDetective decode
deg deleteRows delete display
divi evalInSyncLocalContext exit exp
exportCsv exportSbf exportTab expt
fact fdisplay fileClose fileCopy
fileDisplay fileErase fileOpen fileReadRecord
fileRead fileResize fileSeek fileWrite
filewriteln findBlock find floor
fraction freeBlock gcd getRecursionCount
getTickCount hashString hour iadd
icompareEQ icompareGE icompareGT icompareLE
icompareLT icompareNE idiv imod
importCsv importSbf importTab imul
insertRows insert inside inspect
integer isAtom isBitVector isBoolean
isBound isByteVector isChar isCharacter
isComplex isDate isDictionary isDirectory
isEqual isError isEven isExact
isFloatVector isIdentical isInexact isInside
isIntegerVector isInteger isLambda isMatrix
isMember isMoney isNegative isNull
isNumberMatrix isNumberVector isNumber isObjectVector
isObject isOdd isPair isPcodeVector
isPositive isString isStructure isSymbol
isText isType isVector isZero
isub julian kurtosis lcm
left length list log10
log2 log logbase macroReplace
makeQuotedList max median member
mid min minute mod
modi money month muli
nadd ncompareEQ ncompareGE ncompareGT
ncompareLE ncompareLT ncompareNE ndiv
new nmod nmul now
nsub number objectToMatrix objectToNumMatrix
objectToNumVector pair parent parse
pi preAllocateFixedMemoryBlocks product qt
rad random randomize range
rank refAttributes refValues ref
remove replace rept resize
right round saveObject saveRepository
second setAttributes setBlock setCar
setCdr setLastCdr setq sigmoid
sign sin sinh sizeof
skew sleep sort sql
sqrt srandom stdev stdevp
string sub1 subi submit
substitute substring sum sumsqr
svmRegression system tan tanh
text time type uniqueInsert
var varp vectorBinaryInnerProduct vectorBipolarInnerProduct
vectorCosineInnerProduct vectorCubeInnerProduct vectorDelete vectorExpInnerProduct
vectorFill vectorInnerProduct vectorLogInnerProduct vectorQuartInnerProduct
vectorQuintInnerProduct vectorSigmoidInnerProduct vectorSineInnerProduct vectorSquareInnerProduct
vectorTanInnerProduct vectorTanhInnerProduct writelg writeln

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