{"uid":"cap_RQZfS_bW2tI8TiABPO73g","slug":"numora-statistics-correlation-93c3ff78","name":"Numora Statistics Correlation","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://numormo.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_7VTn3nLHl_QKQsTb7WC2x","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 correlation coefficient between two numeric arrays, quantifying the linear relationship between them","exampleAgentPrompt":"Can you calculate the correlation between these two datasets — x is [1, 2, 3, 4, 5] and y is [2.1, 4.0, 5.9, 8.2, 10.1] — and tell me how strongly they're linearly related?","exampleUseCases":[{"title":"Sales and temperature correlation analysis","prompt":"I have monthly average temperatures [32, 35, 45, 60, 72, 85, 90, 88, 75, 60, 45, 34] and monthly ice cream sales [200, 210, 350, 500, 720, 950, 1050, 1020, 800, 580, 320, 210]. Can you compute how correlated those two series are?"},{"title":"Portfolio return correlation check","prompt":"I want to know the correlation between these two stocks' daily returns — stock A: [0.5, -0.3, 1.2, 0.8, -0.1] and stock B: [0.4, -0.2, 1.0, 0.9, -0.2]. Are they moving together?"},{"title":"Study hours vs exam score relationship","prompt":"I collected data on students: study hours were [1, 2, 3, 4, 5, 6, 7, 8] and their exam scores were [45, 52, 60, 68, 75, 82, 88, 94]. Compute the correlation so I can see how strongly study time predicts score."}],"resultDescription":"Returns a JSON object with a boolean 'success' field, a 'computation' string describing what was calculated, and a 'result' object containing the Pearson correlation coefficient (typically an r-value between -1 and 1) along with any supporting statistical values such as sample size or significance.","failureModes":["Arrays x and y of different lengths — likely returns an error or validation failure","Non-numeric values in arrays — may cause computation failure","Empty arrays provided — endpoint may return error or undefined result","Missing required field x or y — returns 400-level error","Arrays too short to compute meaningful correlation (e.g., single element)"],"whenToPreferThis":"Choose this endpoint when you need a fast, serverless, dependency-free Pearson correlation computation on two numeric arrays without setting up a local math library or data science environment. Ideal for AI agents that need quick statistical checks, micropayment-friendly per-call pricing, and no infrastructure overhead. Prefer over general-purpose code execution when you simply need the correlation coefficient reliably returned as structured JSON.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T18:55:41.926Z","isFirstParty":false}