{"uid":"cap_lrjHuVKy3i8Gl9XtRGcm1","slug":"kihustle-spearman-correlation-api-0d4b2b1a","name":"KiHustle Spearman Correlation API","description":"Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.","url":"https://kihustle.tech/api/v1/json-correlation-spearman","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"series_a":{"type":"array","items":{"type":"number"}},"series_b":{"type":"array","items":{"type":"number"}}}},"responseSchema":{"type":"json","example":{"result":"processed","status":"success"}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.002","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"probe","requiresHandshake":false,"reviewCount":0,"rating":{"score":"0.00","successRate":"0.00","reviews":0,"stars":null,"state":"unrated"},"availabilityStatus":"unknown","priceObserved":null,"sessionDeposit":null,"pricing":{"kind":"static","summary":"$0.002/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.002","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_7G-l_oMDJNYvSLR7DKUsd","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.002","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes the Spearman rank-order correlation coefficient between two numerical data series","exampleAgentPrompt":"Can you compute the Spearman correlation between these two data series — series A is [1, 2, 3, 4, 5] and series B is [5, 6, 7, 8, 7] — and tell me how strongly they're related?","exampleUseCases":[{"title":"Revenue vs ad spend correlation","prompt":"I have monthly revenue figures [12000, 15000, 14000, 18000, 22000] and corresponding ad spend [1000, 1200, 1100, 1500, 2000] — can you run a Spearman correlation to see if there's a meaningful rank relationship between them?"},{"title":"User engagement rank correlation","prompt":"Check the Spearman correlation between my app's daily active users [300, 450, 420, 500, 610] and support tickets [10, 18, 15, 22, 30] — I want to know if higher engagement tends to rank alongside more support load."},{"title":"Survey response consistency check","prompt":"I've got two raters who scored the same 6 candidates: rater A gave [7, 5, 9, 3, 6, 8] and rater B gave [6, 4, 8, 2, 7, 9] — can you calculate the Spearman correlation to see how consistently they're ranking people?"}],"resultDescription":"Returns a JSON object containing a 'result' field with the computed Spearman correlation value (a number between -1 and 1) and a 'status' field confirming success. A value near 1 indicates strong positive rank correlation, near -1 strong negative, and near 0 no monotonic relationship.","failureModes":["Mismatched array lengths between series_a and series_b causing a processing error","Empty arrays provided resulting in undefined correlation","Non-numeric values in arrays causing parsing failure","Constant series (no rank variation) leading to undefined or NaN correlation","Payment failure due to insufficient USDC balance blocking the request"],"whenToPreferThis":"Choose this endpoint when you need a non-parametric rank-order correlation that is robust to outliers and non-normal distributions. Prefer Spearman over Pearson when your data may not be linearly related or when you're working with ordinal data. This is a simple, inexpensive per-call computation suitable for two numeric arrays of any real-world measurement.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:52:17.875Z","isFirstParty":false}