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Variance ​

Generated from the engine's own definition: compute-engine's symbol, which we neither extend nor document by hand.

Sample variance of a collection of numbers.

✓ checked against that system · ! checked, with disagreements (hover a chip)Wolfram LanguageVariance✓
Variance((collection<any> | distribution | number)+) -> nan | real<0..>Sample variance of a collection of numbers.
SageMathvariance(collection<any> | distribution | number)
Typed signatures
TypePackageApplies
((collection<nothing | number> | distribution | number)+) -> nan | real<0..>compute-enginealways
((collection<nothing | number> | distribution | number)+) -> nan | real<0..>statisticsreplaces compute-enginealways

Domain: Compute engine

Examples

Scope #

The sample variance of 1 to 4 is 53\frac53

live ↗
InVariance([1,2,3,4])\mathrm{Variance}(\bigl\lbrack1, 2, 3, 4\bigr\rbrack)Out53\frac{5}{3}

A 30-digit numeric list gives a 30-digit result

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InVariance(N([1,2,3,4],30))\mathrm{Variance}(N(\bigl\lbrack1, 2, 3, 4\bigr\rbrack, 30))Out1.666 666 666 666 666 666 671.666\,666\,666\,666\,666\,666\,67

An unbound data symbol keeps the result symbolic

live ↗
InN(Variance(ts))N(\mathrm{Variance}(\mathrm{ts}))OutVariance(ts)\mathrm{Variance}(\mathrm{ts})

For a binomial it is np(1−p)np(1-p)

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InVariance(BinomialDistribution⁡(n,p))\mathrm{Variance}(\operatorname{BinomialDistribution}(n, p))Outnp×(1−p)np×(1-p)

A Cauchy-like density is left unevaluated

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InVariance(ProbabilityDistribution(2π((x−2)4+1),[x,−∞,∞]))\mathrm{Variance}(\mathrm{ProbabilityDistribution}(\frac{\sqrt{2}}{π((x-2)^4+1)}, \bigl\lbrack x, -∞, ∞\bigr\rbrack))OutVariance(ProbabilityDistribution(2π(x−2)4+π,[x,−∞,∞]))\mathrm{Variance}(\mathrm{ProbabilityDistribution}(\frac{\sqrt{2}}{π(x-2)^4+π}, \bigl\lbrack x, -∞, ∞\bigr\rbrack))
Applications #

N cannot evaluate a symbolic dataset, so both entries stay

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InN([mean(data),Variance(data)])N(\bigl\lbrack\mathrm{mean}(\mathrm{data}), \mathrm{Variance}(\mathrm{data})\bigr\rbrack)Out[mean(data),Variance(data)]\bigl\lbrack\mathrm{mean}(\mathrm{data}), \mathrm{Variance}(\mathrm{data})\bigr\rbrack

Likewise a symbolic dataset stays unevaluated under N

live ↗
InN(Variance(heights))N(\mathrm{Variance}(\mathrm{heights}))OutVariance(heights)\mathrm{Variance}(\mathrm{heights})
About
Examples7
Checked againstWolfram Language 7 agreeSageMath 2 agree3 errorOscar 2 agree
CrosswalkWolfram LanguageSageMath

Implementation

mappedoscarpackages/oracle/src/mappings.ts
mappedsagepackages/oracle/src/mappings.ts