{"uid":"cap_DnhUcLijANn3mU5XI0mtd","slug":"numeric-precision-comparisons-iqr-outlier-flags-two-measure-correlation-1888eb93","name":"Numeric Precision Comparisons — IQR Outlier Flags + Two-Measure Correlation","description":"Numeric precision comparisons — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation for supplied numeric precision comparisons records (source_value, transformed_value). Up to 200 rows; 2 independent reports using shared local analytical engines. No live lookup, prediction, professional assessment or external action. 0.01 USDC includes 2 supplied-data reports for up to 200 rows.","url":"https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-176?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","source_value","transformed_value"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"transformed_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_value":10,"dataset_group":"group-a","transformed_value":1},{"date":"2026-10-02","row_id":"row-2","source_value":20,"dataset_group":"group-b","transformed_value":2},{"date":"2026-10-03","row_id":"row-3","source_value":15,"dataset_group":"group-a","transformed_value":3},{"date":"2026-10-04","row_id":"row-4","source_value":30,"dataset_group":"group-b","transformed_value":4}],"maxItems":200,"minItems":1},"field":{"enum":["source_value","transformed_value"],"type":"string","default":"source_value"}}},"responseSchema":null,"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.01","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":"$0.01/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.01","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ppS_vpGi0rsZLEizO65cg","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.01","costPer":"request","priority":0,"asset":"0x833589fCD6eDb6E08f4c7C32D4f71b54bdA02913","unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Runs IQR-based outlier detection and Pearson/Spearman correlation analysis on supplied source_value vs transformed_value pairs, returning two independent statistical reports for up to 200 rows.","exampleAgentPrompt":"Can you check my dataset for outliers and run a correlation analysis? I have up to 50 rows with a source_value and transformed_value per row — flag which rows are IQR outliers and tell me how correlated the two measures are.","exampleUseCases":[{"title":"ETL pipeline data quality audit","prompt":"I just ran a transformation on 80 rows of sensor readings and I want to make sure nothing went wrong — can you flag any IQR outliers in the source_value column and also check how well source_value and transformed_value correlate across all rows?"},{"title":"Finance reconciliation anomaly check","prompt":"I have 120 rows of reported vs adjusted revenue figures by date and dataset group — please flag any IQR outliers in the adjusted figures and give me a correlation report between the reported and adjusted amounts."},{"title":"ML feature preprocessing validation","prompt":"Before I feed this into my model, can you run an IQR outlier check on the raw feature values and a two-measure correlation against the normalized values for my 200-row dataset? I want to catch any suspicious rows."}],"resultDescription":"Returns two independent statistical reports: (1) IQR outlier flags identifying which rows fall outside the interquartile range bounds for the selected field, and (2) a correlation report quantifying the relationship between source_value and transformed_value across all supplied rows. Up to 200 rows processed per call.","failureModes":["Fewer than 1 row supplied — minimum row count not met","More than 200 rows supplied — exceeds maximum batch limit","source_value or transformed_value outside allowed range [-1e12, 1e12]","Invalid date format — must match YYYY-MM-DD pattern","row_id missing or empty — required field not provided","All null values in selected field — IQR computation not possible","Payment not processed — x402 payment required at $0.01 USDC"],"whenToPreferThis":"Choose this endpoint when you need a fast, local, self-contained statistical check on a small-to-medium numeric dataset (up to 200 rows) with paired source and transformed values. Ideal for data pipeline validation, ETL auditing, or preprocessing checks where you want both outlier flagging and correlation in a single $0.01 call. Not suitable for large datasets, real-time streaming data, predictive modeling, or when you need external data lookup.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:02.045Z","isFirstParty":false,"canonicalSlug":"numeric-precision-comparisons-iqr-outlier-flags-two-measure-correlation-1888eb93"}