{"uid":"cap_YjhQ07zMPCLLprhYIPgsU","slug":"sampling-observations-grouped-numeric-summary-iqr-outlier-flags-a9743b27","name":"Sampling Observations — Grouped Numeric Summary + IQR Outlier Flags","description":"Sampling observations — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags for supplied sampling observations records (sample_value, sample_weight). 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-15-094?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","dataset_group","date","sample_value","sample_weight"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"sample_value":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"dataset_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","sample_value":10,"dataset_group":"group-a","sample_weight":1},{"date":"2026-10-02","row_id":"row-2","sample_value":20,"dataset_group":"group-b","sample_weight":2},{"date":"2026-10-03","row_id":"row-3","sample_value":15,"dataset_group":"group-a","sample_weight":3},{"date":"2026-10-04","row_id":"row-4","sample_value":30,"dataset_group":"group-b","sample_weight":4}],"maxItems":200,"minItems":1},"field":{"enum":["sample_value","sample_weight"],"type":"string","default":"sample_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_8y9erPMo4AfFtmNOczJTL","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":"Computes grouped descriptive statistics and IQR-based outlier flags for up to 200 sampling observation records with sample_value and sample_weight fields.","exampleAgentPrompt":"Can you run a grouped numeric summary with IQR outlier flags on these sampling observations? I have up to 50 rows each with a row_id, dataset_group, date, sample_value, and sample_weight — I want to analyze the sample_value field grouped by category.","exampleUseCases":[{"title":"Environmental sampling QA by site","prompt":"I have 120 water quality sampling records, each with a site group, collection date, measured concentration as sample_value, and a sample_weight. Can you group them by site and flag any IQR outliers in the sample_value so I can identify problematic measurement stations?"},{"title":"Clinical trial weight-adjusted value review","prompt":"Run a grouped numeric summary with outlier flags on my clinical trial observation data — about 80 rows with patient_group as dataset_group, sample_value as the lab measurement, and sample_weight as the patient weighting. Flag anything that looks like an IQR outlier."},{"title":"Manufacturing batch quality inspection","prompt":"I've got 150 product inspection records organized by production batch (dataset_group), each with a measured defect score (sample_value) and batch size (sample_weight). Can you give me a grouped summary and tell me which records are flagged as IQR outliers?"}],"resultDescription":"Returns two analytical reports: (1) a grouped numeric summary with descriptive statistics (mean, median, min, max, count, etc.) broken down by dataset_group, and (2) IQR-based outlier flags identifying which individual rows fall outside the interquartile range for their group — all computed locally without external lookups.","failureModes":["Input array exceeds 200 rows — request rejected","Missing required fields (row_id, dataset_group, date, sample_value, sample_weight) causes validation error","Invalid date format (must match YYYY-MM-DD pattern) causes rejection","All sample_value or sample_weight fields null for a group prevents meaningful summary","Numeric values outside allowed range (-1e12 to 1e12) cause schema validation failure"],"whenToPreferThis":"Choose this endpoint when you have tabular sampling observation data (up to 200 rows) that needs grouped descriptive statistics and IQR-based outlier detection in a single call. It is ideal for quality assurance workflows, survey data review, scientific measurement analysis, or any scenario where you need to both summarize and flag extreme values across predefined categories, without requiring external data sources or prediction models.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:59.256Z","isFirstParty":false,"canonicalSlug":"sampling-observations-grouped-numeric-summary-iqr-outlier-flags-a9743b27"}