Dispersion, Shape, and Robust Statistics
10 algorithms in Financial Mathematics, Statistics, and Data Foundations · 10 with asserted arithmetic.
In this family#
-
Deviation, Absolute Deviation, and Squared Deviation verified
deviationAbsoluteDeviationAndSquaredDeviation(input) -
Range and Interquartile Range verified
rangeAndInterquartileRange(input) -
Mean Absolute Deviation verified
meanAbsoluteDeviation(input) -
Median Absolute Deviation verified
medianAbsoluteDeviation(input) -
Population and Sample Variance verified
populationAndSampleVariance(input) -
Population and Sample Standard Deviation verified
populationAndSampleStandardDeviation(input) -
Coefficient of Variation and Scale Comparability verified
coefficientOfVariationAndScaleComparability(input) -
Z-Score, Robust Z-Score, and Standardization verified
zScoreRobustZScoreAndStandardization(input) -
Skewness and Tail Asymmetry verified
skewnessAndTailAsymmetry(input) -
Kurtosis, Excess Kurtosis, and Tail Weight verified
kurtosisExcessKurtosisAndTailWeight(input)
What they share#
Every topic here is a record-transform, so once you have
called one the rest follow the same shape. Import paths differ only in the final segment:
import { deviationAbsoluteDeviationAndSquaredDeviation } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/deviation-absolute-deviation-and-squared-deviation";
import { rangeAndInterquartileRange } from "fintech-algorithms/financial-mathematics-statistics-and-data-foundations/dispersion-shape-and-robust-statistics/range-and-interquartile-range";Read them in the order above — the sequence is pedagogical, not alphabetical.
Where this sits#
Financial Mathematics, Statistics, and Data Foundations collects 120 algorithms across 12 families. For the concept behind this family rather than the call signatures, see the concept guides.