{"uid":"cap_19formS1XrElwOFc_XqLB","slug":"damaged-stock-iqr-outlier-flags-secondary-measure-weighted-mean-5f6ef327","name":"Damaged Stock IQR Outlier Flags + Secondary-Measure Weighted Mean","description":"Damaged stock — IQR outlier flags + Secondary-measure weighted mean: IQR outlier flags + Secondary-measure weighted mean for supplied damaged stock records (handled_units, damaged_units). 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-retail-18-177?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","store_group","date","handled_units","damaged_units"],"properties":{"date":{"type":"string","default":"","pattern":"^\\d{4}-\\d{2}-\\d{2}$","maxLength":10},"row_id":{"type":"string","default":"","maxLength":80,"minLength":1},"store_group":{"type":"string","default":"","maxLength":80},"damaged_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"handled_units":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","store_group":"group-a","damaged_units":1,"handled_units":10},{"date":"2026-10-02","row_id":"row-2","store_group":"group-b","damaged_units":2,"handled_units":20},{"date":"2026-10-03","row_id":"row-3","store_group":"group-a","damaged_units":3,"handled_units":15},{"date":"2026-10-04","row_id":"row-4","store_group":"group-b","damaged_units":4,"handled_units":30}],"maxItems":200,"minItems":1},"field":{"enum":["handled_units","damaged_units"],"type":"string","default":"handled_units"}}},"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_cX2j7fLfcBSb30FKsyNEX","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 secondary-measure weighted mean analysis on up to 200 rows of damaged stock records (handled_units, damaged_units), returning two independent analytical reports.","exampleAgentPrompt":"I have up to 200 rows of damaged stock data with handled_units and damaged_units per store group and date — can you run IQR outlier flagging and a secondary-measure weighted mean on the damaged_units field and give me both reports?","exampleUseCases":[{"title":"Weekly damage audit across store groups","prompt":"I've got this week's damaged stock records for our store groups — each row has a row ID, store group, date, handled units, and damaged units. Can you flag any IQR outliers and compute the weighted mean on the damaged_units field so I can identify which groups had abnormal damage this week?"},{"title":"Identify anomalous warehouse damage events","prompt":"Here are 150 rows of warehouse damage logs across several distribution centers. Run IQR outlier detection and a weighted mean analysis on handled_units so I can spot any days or locations where handling was statistically unusual."},{"title":"Quality control check on retail damage data","prompt":"Before I present these damaged stock numbers to management, can you run the IQR outlier flags and secondary-measure weighted mean on the damaged_units field for all 80 rows so I know which records are statistical anomalies I should double-check?"}],"resultDescription":"Two independent analytical reports: (1) IQR outlier flags identifying which of the up to 200 submitted rows are statistical outliers based on interquartile range for the selected field, and (2) a secondary-measure weighted mean computed across the supplied damaged stock records. No external lookups or predictions are made — all analysis is performed locally on the submitted data.","failureModes":["Submitting more than 200 rows returns an error or truncation","Missing required fields (row_id, date, handled_units, damaged_units) causes validation failure","Invalid date format (not YYYY-MM-DD) rejected by schema","Null values for both handled_units and damaged_units on a row may produce incomplete statistical results","Payment of 0.01 USDC not completed results in 402 response and no analysis"],"whenToPreferThis":"Choose this endpoint when you have up to 200 rows of damaged stock records and need both IQR-based outlier detection and a weighted mean computation in a single paid call. It is purpose-built for retail/warehouse damage data with handled_units and damaged_units fields, and delivers two bundled statistical reports cheaply ($0.01 USDC). Prefer it over generic statistics tools when your data fits the retail damaged-stock schema and you want a structured, reproducible output without setting up a local analytics pipeline.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-24T12:51:02.074Z","isFirstParty":false,"canonicalSlug":"damaged-stock-iqr-outlier-flags-secondary-measure-weighted-mean-5f6ef327"}