{"uid":"cap_kktwAReCJg0HuK1P-c6_4","slug":"sampling-observations-numeric-distribution-iqr-outlier-flags-a1f4d26d","name":"Sampling Observations — Numeric Distribution + IQR Outlier Flags","description":"Sampling observations — Numeric distribution + IQR outlier flags: Numeric distribution + 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-15-080?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_yXBZiz1GqG3vyNMPExgrO","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 numeric distribution statistics and IQR-based outlier flags for up to 200 sampling observation records (sample_value or sample_weight).","exampleAgentPrompt":"Analyze the distribution of my sampling observations and flag any IQR outliers in the sample_value field — here are my 12 records: row-1 on 2025-01-10 with sample_value 42 and sample_weight 1, row-2 on 2025-01-11 with sample_value 200 and sample_weight 2, row-3 on 2025-01-12 with sample_value 38 and sample_weight 1, and so on for all 12 rows in group-a.","exampleUseCases":[{"title":"Environmental sampling outlier review","prompt":"I have 50 water quality measurements collected this quarter — can you analyze the distribution of sample_value and flag any IQR outliers? Each row has a row_id, collection date, dataset_group set to 'water-q1', sample_value for the contaminant level, and sample_weight of 1 for each."},{"title":"Weighted survey response analysis","prompt":"Can you run a numeric distribution and IQR outlier analysis on my 80 survey responses? I want to analyze the sample_weight field — respondents have different weights based on demographics. Groups are 'urban' and 'rural', dates run from 2025-03-01 to 2025-05-31."},{"title":"Manufacturing QC measurement audit","prompt":"I've got 150 production line measurements I need analyzed — run a distribution report and flag IQR outliers on the sample_value column. The dataset_group is 'line-b' and measurements were taken daily from 2025-06-01 onward."}],"resultDescription":"Returns two independent analytical reports: (1) a numeric distribution summary (e.g., mean, median, quartiles, min, max, std dev) and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range fence — all computed locally on the supplied data without external lookup.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — exceeds row limit, request rejected","Invalid date format (not YYYY-MM-DD) — schema validation failure","sample_value or sample_weight out of allowed range (±1,000,000,000,000) — validation error","Invalid field selector (not 'sample_value' or 'sample_weight') — enum validation failure","Missing required row fields (row_id, dataset_group, date) — schema validation error"],"whenToPreferThis":"Choose this endpoint when you need both a numeric distribution summary and IQR outlier detection on a batch of up to 200 sampling observation records in a single call. It is ideal for scientific, environmental, or quality-control datasets with a sample_value and sample_weight structure. Prefer it over generic statistics APIs when you need two coordinated reports (distribution + outlier flags) computed together at low cost ($0.01 USDC) with no external data access or side effects.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-19T12:47:08.601Z","isFirstParty":false,"canonicalSlug":"sampling-observations-numeric-distribution-iqr-outlier-flags-a1f4d26d"}