{"uid":"cap_XsnQXW524549TrVsGsyRj","slug":"kihustle-pearson-correlation-coefficient-calculator-cded7202","name":"KiHustle Pearson Correlation Coefficient 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/agents/api/v1/json-correlation-pearson","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_RNxJyBXPNdjZm2ySxphkS","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 Pearson correlation coefficient between two numeric data series","exampleAgentPrompt":"Calculate the Pearson correlation coefficient between these two series: series A is [1, 2, 3, 4, 5] and series B is [2.1, 4.0, 5.9, 8.1, 10.0] — I want to know how strongly they're linearly related.","exampleUseCases":[{"title":"Sales vs. advertising spend correlation","prompt":"I have monthly sales figures [12000, 15000, 13500, 17000, 20000] and the corresponding ad spend [1000, 1400, 1200, 1600, 2100] — calculate the Pearson correlation so I can see how tightly ad budget drives revenue."},{"title":"Temperature vs. energy consumption analysis","prompt":"Can you compute the Pearson correlation between daily high temperatures [72, 78, 85, 90, 88, 76] and daily electricity usage [310, 340, 390, 430, 415, 325]? I want to understand how strongly heat drives energy demand."},{"title":"Student test score relationship study","prompt":"I've got two sets of student scores — midterm grades [55, 67, 72, 80, 91, 88] and final exam grades [58, 65, 74, 83, 89, 92] — can you run a Pearson correlation to see if mid-term performance predicts final results?"}],"resultDescription":"Returns a JSON object with a 'result' field containing the computed Pearson correlation coefficient (a value between -1 and 1 indicating the strength and direction of the linear relationship) and a 'status' field indicating success or failure of the computation.","failureModes":["Mismatched array lengths between series_a and series_b causing computation error","Empty arrays or single-element arrays that make correlation undefined","Non-numeric values in either series causing parsing failure","Zero-variance series (all identical values) causing division-by-zero in Pearson formula","Missing series_a or series_b in request body"],"whenToPreferThis":"Choose this endpoint when you need a quick, paid-per-call Pearson correlation computation without setting up your own statistical library or running local code. Ideal for AI agents that need on-demand correlation analysis as part of a larger workflow without a dedicated data science backend. At $0.002 per call it is cost-effective for occasional or moderate-volume correlation checks.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-13T19:02:59.823Z","isFirstParty":false}