{"uid":"cap_gTYGnjDk_IJfRTK5Wdltd","slug":"join-coverage-iqr-outlier-flags-two-measure-correlation-analyzer-a6c72b62","name":"Join Coverage IQR Outlier Flags + Two-Measure Correlation Analyzer","description":"Join coverage — IQR outlier flags + Two-measure correlation: IQR outlier flags + Two-measure correlation for supplied join coverage records (matched_rows, source_rows). 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-09-176?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","matched_rows","source_rows"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"source_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"matched_rows":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","source_rows":1,"matched_rows":10,"dataset_group":"group-a"},{"date":"2026-10-02","row_id":"row-2","source_rows":2,"matched_rows":20,"dataset_group":"group-b"},{"date":"2026-10-03","row_id":"row-3","source_rows":3,"matched_rows":15,"dataset_group":"group-a"},{"date":"2026-10-04","row_id":"row-4","source_rows":4,"matched_rows":30,"dataset_group":"group-b"}],"maxItems":200,"minItems":1},"field":{"enum":["matched_rows","source_rows"],"type":"string","default":"matched_rows"}}},"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_VnuFwJKIwH2b3KNHGEBik","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 two-measure Pearson/rank correlation on up to 200 join coverage records (matched_rows, source_rows), returning two independent analytical reports.","exampleAgentPrompt":"I have a list of join coverage records with matched_rows and source_rows values across different dataset groups — can you run IQR outlier detection and a two-measure correlation analysis on them? Here are the rows: [{\"row_id\":\"r1\",\"date\":\"2025-01-01\",\"dataset_group\":\"orders\",\"matched_rows\":150,\"source_rows\":200},{\"row_id\":\"r2\",\"date\":\"2025-01-02\",\"dataset_group\":\"orders\",\"matched_rows\":5,\"source_rows\":210}], and flag outliers on the matched_rows field.","exampleUseCases":[{"title":"ETL pipeline join quality audit","prompt":"I'm auditing our ETL pipeline and want to check if any of our daily join coverage records look like outliers — here are 30 rows with matched_rows and source_rows across two dataset groups (orders and customers), can you run IQR outlier flagging and also correlate matched_rows against source_rows for me?"},{"title":"Data drift detection across dataset groups","prompt":"We've noticed some weirdness in our join rates lately — can you take these 50 join coverage records from the last two months and tell me which matched_rows values are IQR outliers, plus show me how correlated matched_rows and source_rows are across our 'transactions' and 'accounts' groups?"},{"title":"Pre-release data quality gate check","prompt":"Before we release this dataset, I need a quick statistical sanity check — run IQR outlier detection on source_rows for these 80 join coverage records and also compute the two-measure correlation between matched_rows and source_rows so I know if there's a systematic issue."}],"resultDescription":"Two independent analytical reports: (1) IQR outlier flags for each row indicating whether the selected field (matched_rows or source_rows) falls outside the interquartile range boundaries, including computed Q1/Q3/IQR thresholds; (2) a two-measure correlation report quantifying the statistical relationship between matched_rows and source_rows across the supplied records, including the correlation coefficient and relevant summary statistics. Both reports are computed locally from the supplied data with no external lookups.","failureModes":["Fewer than 1 row supplied — minimum 1 row required","More than 200 rows submitted — exceeds maxItems limit and will be rejected","Missing required fields (row_id, dataset_group, date, matched_rows, source_rows) on any row","Date field not matching YYYY-MM-DD pattern — validation error","Null values in both measure columns may degrade or invalidate correlation computation","Payment of 0.01 USDC not completed — x402 payment required before response","Insufficient variance in data (e.g. all identical values) may produce degenerate IQR or correlation results"],"whenToPreferThis":"Choose this endpoint when you have structured join coverage records with matched_rows and source_rows fields and need both IQR-based outlier detection and a correlation report in a single call at minimal cost. Ideal for data engineers and analytics pipelines that need lightweight, self-contained statistical QA on join metrics without spinning up a full analytics environment. Prefer this over generic stats APIs when your data already conforms to the join-coverage schema (row_id, dataset_group, date, matched_rows, source_rows) and you want two reports bundled for $0.01.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-23T02:01:48.872Z","isFirstParty":false,"canonicalSlug":"join-coverage-iqr-outlier-flags-two-measure-correlation-analyzer-a6c72b62"}