{"uid":"cap_BHp8ACg2dZuMFdZDsbAHv","slug":"numora-statistics-correlation-endpoint-b3f8d953","name":"Numora Statistics Correlation Endpoint","description":"100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.","url":"https://numomo.vercel.app/api/statistics/correlation","method":"POST","headers":{},"bodySchema":{"type":"object","required":["x","y"],"properties":{"x":{"type":"array"},"y":{"type":"array"}}},"responseSchema":{"type":"object","required":["success","result","computation"],"properties":{"result":{"type":"object","description":"Computation result varies by endpoint"},"success":{"type":"boolean","description":"Always true on success"},"computation":{"type":"string","description":"Human-readable description of what was computed"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_DlnvMEIEyVi7g3S0P0t46","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Computes the Pearson or statistical correlation coefficient between two numerical arrays","exampleAgentPrompt":"Can you compute the statistical correlation between these two datasets — x is [10, 20, 30, 40, 50] and y is [12, 24, 28, 45, 52]?","exampleUseCases":[{"title":"Sales vs. ad spend correlation","prompt":"I have monthly sales figures [5000, 5800, 6200, 7100, 8000] and monthly ad spend [1000, 1200, 1300, 1500, 1700] — can you calculate the correlation between them so I know how closely ad budget drives revenue?"},{"title":"Climate data relationship check","prompt":"Run a correlation between these temperature readings [15, 18, 22, 27, 30] and ice cream sales counts [200, 280, 350, 420, 510] to see how strongly heat drives sales."},{"title":"Portfolio returns correlation analysis","prompt":"Can you compute the correlation between stock A's weekly returns [0.02, -0.01, 0.03, 0.015, -0.005] and stock B's returns [0.018, -0.008, 0.025, 0.012, -0.003] to assess how diversified my portfolio is?"}],"resultDescription":"Returns a JSON object with a 'success' boolean (always true on success), a 'result' object containing the computed correlation coefficient and related statistics, and a 'computation' string describing in plain English what was calculated.","failureModes":["Arrays x and y have different lengths — returns an error","Non-numeric values in input arrays cause computation failure","Empty arrays provided — returns validation error","Missing required field x or y — returns 400-level error","Micropayment failure via x402 protocol prevents request from being processed"],"whenToPreferThis":"Use this endpoint when you need a fast, serverless, dependency-free computation of the statistical correlation between two numeric arrays without setting up a local math library. Ideal for AI agents needing a lightweight math microservice with per-call micropayment pricing on Base, especially in environments where installing scipy or numpy is not feasible.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-15T12:57:27.342Z","isFirstParty":false}