{"uid":"cap_L7szgq-XycdgfZS3TJ_9v","slug":"sampling-observations-numeric-row-ordering-iqr-outlier-flags-e7fb4fb9","name":"Sampling Observations — Numeric Row Ordering + IQR Outlier Flags","description":"Sampling observations — Numeric row ordering + IQR outlier flags: Numeric row ordering + 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-15-119?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_G8lGzt5aBrF0SlycTnSUk","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":"Analyzes supplied sampling observation records by ordering rows numerically and flagging outliers using the interquartile range (IQR) method, producing two analytical reports for up to 200 rows.","exampleAgentPrompt":"I have a set of sampling observations with values and weights — can you rank them numerically and flag any IQR outliers in the sample_value field? Here are up to 50 rows with row IDs, dates, dataset groups, sample values, and sample weights.","exampleUseCases":[{"title":"Survey data quality check","prompt":"I have 80 survey response records with sample values and weights — can you rank them by sample_value and flag any outliers using IQR so I can see which responses look anomalous before I run my analysis?"},{"title":"Environmental monitoring outlier detection","prompt":"Here are 150 air quality sampling measurements with dates and sensor group IDs — please order them numerically by sample_value and flag any IQR outliers so I can identify faulty sensor readings."},{"title":"Lab sample weight validation","prompt":"I've got 60 lab sample records across two groups and I want to check the sample_weight field — can you rank the rows and flag any IQR-based outliers so I know which weights are suspiciously high or low?"}],"resultDescription":"Two analytical reports covering: (1) numeric ordering/ranking of the submitted rows by the selected field (sample_value or sample_weight), and (2) IQR-based outlier flags identifying which rows fall outside the interquartile range boundaries. No external data is fetched; analysis is computed entirely from the supplied rows.","failureModes":["Fewer than 1 row submitted — validation error","More than 200 rows submitted — rejected, max items exceeded","Invalid date format (not YYYY-MM-DD) — schema validation failure","Missing required fields (row_id, dataset_group, date) — request rejected","All values are null for the selected field — IQR computation may be undefined","Payment of 0.01 USDC not provided — x402 payment required error"],"whenToPreferThis":"Choose this endpoint when you have up to 200 sampling observation records with numeric values and weights and need both a ranked ordering and IQR-based outlier detection in a single call. It is ideal for lightweight statistical QA on survey, lab, or environmental sample data without needing a full analytics platform. Prefer this over general-purpose analytics tools when you want a low-cost ($0.01), self-contained, no-external-lookup analysis of already-collected sample data.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T12:38:19.606Z","isFirstParty":false,"canonicalSlug":"sampling-observations-numeric-row-ordering-iqr-outlier-flags-e7fb4fb9"}