{"uid":"cap_BiU6HiyQuLZqnqryKoveV","slug":"kihustle-covariance-calculator-10ffea28","name":"KiHustle Covariance Calculator","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/bot-bazaar/api/v1/covariance-calculator?utm_source=zero.xyz","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_8ViasiQuBYRA-Oix0uaMY","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 statistical covariance between two numerical data series","exampleAgentPrompt":"Calculate the covariance between these two data series for me: series A is [2.1, 3.5, 4.8, 6.2, 5.9] and series B is [1.3, 2.7, 3.1, 5.4, 4.8].","exampleUseCases":[{"title":"Portfolio correlation analysis","prompt":"I need to know the covariance between my two stock return series — series A is [0.05, 0.02, -0.01, 0.08, 0.03] and series B is [0.04, 0.01, -0.02, 0.07, 0.02] — to see how closely they move together."},{"title":"Scientific experiment data comparison","prompt":"Can you compute the covariance between my two measurement arrays? Series A is [12.3, 14.1, 13.8, 15.2, 11.9] and series B is [8.4, 9.2, 8.9, 10.1, 7.8]."},{"title":"Sales and marketing spend relationship","prompt":"I have monthly sales figures [50000, 62000, 58000, 71000, 65000] and marketing spend [8000, 10000, 9500, 12000, 11000] — calculate the covariance so I can see if they trend together."}],"resultDescription":"Returns a JSON object with a 'result' field containing the computed covariance value and a 'status' field indicating success or failure of the computation.","failureModes":["Mismatched array lengths between series_a and series_b may cause computation errors","Empty arrays or arrays with a single element may produce undefined or zero covariance","Non-numeric values in the arrays will likely cause validation failures","Extremely large arrays may affect response time or cause timeout","Vague or missing input fields return a generic error response due to minimal schema documentation"],"whenToPreferThis":"Use this endpoint when you need a quick, cheap ($0.002 per call) stateless covariance computation between exactly two numeric series without setting up a local stats library or full data pipeline. It is best suited for lightweight, on-demand statistical checks in automated workflows or agent pipelines where installing scipy or numpy is impractical.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-10T12:43:20.991Z","isFirstParty":false,"canonicalSlug":"kihustle-covariance-calculator-858a2350"}