{"uid":"cap_KoSpQyDu_KsJ9J16hDnu5","slug":"numora-statistics-outlier-detection-api-62095f13","name":"Numora Statistics Outlier Detection API","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/outlier","method":"POST","headers":{},"bodySchema":{"type":"object","required":["data"],"properties":{"data":{"type":"array"},"method":{"enum":["iqr","zscore"],"type":"string"}}},"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_qjQW-sm2JZUFORI5nobD-","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":"Detects outliers in a numeric dataset using IQR or Z-score statistical methods","exampleAgentPrompt":"Can you find the outliers in this dataset [2, 3, 4, 5, 100, 3, 4, 2, 98, 3] using the IQR method?","exampleUseCases":[{"title":"Sensor data anomaly detection","prompt":"I have a list of temperature sensor readings [20.1, 21.3, 19.8, 20.5, 85.2, 21.0, 20.7, 19.9] — can you flag any outliers using the z-score method so I know which ones to discard?"},{"title":"Financial data quality check","prompt":"Here are daily transaction amounts for a user: [50, 45, 60, 55, 48, 5000, 52, 47] — can you detect any outliers using IQR to help me spot potentially fraudulent transactions?"},{"title":"Student grade distribution review","prompt":"I have these student test scores: [72, 75, 68, 80, 74, 12, 77, 73, 71, 99] — can you identify statistical outliers using the z-score method so I can review those edge cases?"}],"resultDescription":"Returns a JSON object with a success flag, a human-readable computation description, and a result object containing identified outlier values, their indices in the original array, and relevant statistical thresholds (e.g. IQR bounds or Z-score cutoffs) used to classify them.","failureModes":["Empty or missing data array returns validation error","Non-numeric values in array cause computation failure","Invalid method enum value (not 'iqr' or 'zscore') returns error","Single-element arrays may not support meaningful outlier detection","Payment failure via x402 micropayment returns 402 error before computation"],"whenToPreferThis":"Choose this endpoint when you need a deterministic, dependency-free statistical outlier detection on a numeric array without needing to run code locally or invoke a heavy ML service. Ideal for data cleaning pipelines, anomaly flagging in small-to-medium datasets, and situations where you want a choice between classic IQR (robust to skew) and Z-score (assumes normality) methods with a clear, interpretable result.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T06:35:27.117Z","isFirstParty":false}