{"uid":"cap_Rz3b5CAhgvhXZynL5Mg-i","slug":"market-survey-observations-string-normalisation-two-measure-correlation-89d5a5a8","name":"Market Survey Observations — String Normalisation + Two-Measure Correlation","description":"Market survey observations — String normalisation + Two-measure correlation: String normalisation + Two-measure correlation for supplied market survey observations records (respondent_count, 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-company-06-033?utm_source=zero.xyz","method":"POST","headers":{},"bodySchema":{"type":"object","properties":{"rows":{"type":"array","items":{"type":"object","required":["row_id","company_group","date","respondent_count","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},"company_group":{"type":"string","default":"","maxLength":80},"sample_weight":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000},"respondent_count":{"type":["number","null"],"maximum":1000000000000,"minimum":-1000000000000}},"additionalProperties":false},"default":[{"date":"2026-10-01","row_id":"row-1","company_group":"group-a","sample_weight":1,"respondent_count":10},{"date":"2026-10-02","row_id":"row-2","company_group":"group-b","sample_weight":2,"respondent_count":20},{"date":"2026-10-03","row_id":"row-3","company_group":"group-a","sample_weight":3,"respondent_count":15},{"date":"2026-10-04","row_id":"row-4","company_group":"group-b","sample_weight":4,"respondent_count":30}],"maxItems":200,"minItems":1}}},"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_BUAhDVSUhOr7PfLbBk1Rm","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":"Normalises string fields and computes correlation between respondent_count and sample_weight across up to 200 market survey observation rows, returning two analytical reports.","exampleAgentPrompt":"Run string normalisation and a correlation analysis between respondent_count and sample_weight on my market survey data — I have 4 rows with company groups 'group-a' and 'group-b', dated from 2026-10-01 to 2026-10-04, with respondent counts 10, 20, 15, 30 and sample weights 1, 2, 3, 4.","exampleUseCases":[{"title":"Survey panel quality check","prompt":"I have 50 rows of market survey data with company groups, dates, respondent counts, and sample weights — can you normalise the string fields and tell me if there's a meaningful correlation between respondent count and sample weight?"},{"title":"Cross-group survey comparison","prompt":"We ran a brand survey across group-a and group-b over Q4 2026 — can you clean up the group label strings and compute the correlation between how many respondents we had versus their sample weights for all 120 rows?"},{"title":"Weighted response analysis","prompt":"Before I present my survey findings, can you normalise the company group names and run a two-measure correlation between respondent_count and sample_weight on my 80 observation rows so I can check if weighting is aligned with sample size?"}],"resultDescription":"Two analytical reports: (1) a string normalisation report covering the company_group and related string fields across submitted rows, and (2) a Pearson or equivalent correlation report between respondent_count and sample_weight, computed locally without any external lookup or live data.","failureModes":["Exceeds 200 rows limit — request rejected","Missing required fields (row_id, date, respondent_count, sample_weight) — validation error","Null values in both numeric measures preventing correlation computation","Invalid date format not matching YYYY-MM-DD pattern","Empty rows array — minimum 1 row required"],"whenToPreferThis":"Choose this endpoint when you have market survey tabular data and need both string field normalisation and a statistical correlation between respondent count and sample weight in a single low-cost call. Ideal for preprocessing survey exports before reporting, validating weighting schemes, or quickly checking measure relationships without setting up a local analytics environment. At $0.01 USDC for two reports on up to 200 rows, it is cost-effective for routine survey QA pipelines.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-10-15T06:40:12.158Z","isFirstParty":false,"canonicalSlug":"market-survey-observations-string-normalisation-two-measure-correlation-89d5a5a8"}