{"uid":"cap_MxiiW-5GpUeoKfYgW6A6N","slug":"kihustle-spearman-rank-correlation-api-a35be3f9","name":"KiHustle Spearman Rank 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/bot-bazaar/api/v1/json-correlation-spearman?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_krN8_ZPVCIIb2aAWgeuJq","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 correlation coefficient between two numerical data series","exampleAgentPrompt":"Can you calculate the Spearman rank correlation between these two series: [1, 2, 3, 4, 5] and [5, 6, 7, 8, 7]? I want to know how strongly they're monotonically related.","exampleUseCases":[{"title":"Marketing spend vs sales rank correlation","prompt":"I have monthly marketing spend figures [1000, 1500, 2000, 1800, 2500] and corresponding sales rank scores [3, 5, 6, 5, 8] — can you compute the Spearman rank correlation to see if higher spend tends to produce better sales ranks?"},{"title":"Survey response consistency check","prompt":"I ran the same survey twice and got two sets of ordinal ratings from the same respondents: [4, 3, 5, 2, 1] and [4, 2, 5, 3, 1]. Can you compute the Spearman correlation to tell me how consistent respondents were between the two rounds?"},{"title":"Stock ranking correlation over time","prompt":"I have two lists of stock performance ranks from different periods — [1, 5, 3, 7, 2, 6, 4] and [2, 4, 3, 6, 1, 7, 5] — can you run a Spearman rank correlation to see if the top performers tend to stay on top?"}],"resultDescription":"Returns a JSON object with a 'result' field containing the computed Spearman correlation value (ranging from -1 to +1) and a 'status' field indicating whether the computation succeeded. A value near +1 indicates strong positive monotonic correlation, near -1 indicates strong negative monotonic correlation, and near 0 indicates no monotonic relationship.","failureModes":["Mismatched series lengths (series_a and series_b must have equal number of elements)","Empty arrays provided as input","Non-numeric values in either series causing parsing errors","Network timeout or payment processing failure via x402 protocol","Ambiguous or missing input schema fields returning a 400-level error"],"whenToPreferThis":"Choose this endpoint when you need a non-parametric rank-based correlation measure that does not assume a normal distribution or linear relationship — ideal for ordinal data, ranked scores, or data with outliers. Prefer over Pearson correlation when the data may not be normally distributed or when you care about monotonic rather than strictly linear association. At $0.002 per call, it is cost-effective for lightweight statistical computations in automated workflows.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-13T12:30:57.700Z","isFirstParty":false,"canonicalSlug":"kihustle-spearman-rank-correlation-api-a35be3f9"}