{"uid":"cap__HFcOnjOwbHZ-a9YXj91E","slug":"numora-regression-analysis-api-9b85d6f3","name":"Numora Regression Analysis API","description":"100 pure math computation endpoints for AI agents. Statistics, financial math, linear algebra, equation solving, calculus, number theory, sequence generation, and unit conversions. Zero external dependencies. x402 micropayments on Base.","url":"https://numormo.vercel.app/api/statistics/regression","method":"POST","headers":{},"bodySchema":{"type":"object","required":["x","y"],"properties":{"x":{"type":"array"},"y":{"type":"array"},"degree":{"type":"integer"}}},"responseSchema":{"type":"object","required":["success","result","computation"],"properties":{"result":{"type":"object","description":"Computation result varies by endpoint"},"success":{"type":"boolean","description":"Always true on success"},"computation":{"type":"string","description":"Human-readable description of what was computed"}}},"example":null,"exampleRequest":null,"tags":["x402"],"displayCostAmount":"0.1","displayCostAsset":"USDC","priceDynamic":false,"priceHint":null,"priceStatus":"priced","priceSource":"registry","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":"$0.1/call","primary":{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"},"accepted":[{"kind":"static","protocol":"x402","network":"base","amountUsd":"0.1","per":"call","confidence":"exact"}]},"paymentMethods":[{"uid":"pm_ImdC2KOHxO4TgV4sq5CvZ","protocol":"x402","methodType":"crypto","chain":"base","mode":"charge","costAmount":"0.1","costPer":"request","priority":0,"asset":null,"unit":"request","depositMicros":null,"planRef":null}],"brandName":null,"brandSlug":null,"brandBaseUrl":null,"brandDocsUrl":null,"whatItDoes":"Fits a polynomial regression model to paired x/y data arrays and returns regression coefficients and model statistics","exampleAgentPrompt":"Can you fit a quadratic (degree 2) polynomial regression to my data — x values are [1, 2, 3, 4, 5] and y values are [2.1, 3.9, 9.2, 15.8, 25.1]?","exampleUseCases":[{"title":"Trend line for sales data","prompt":"I have monthly sales figures — x is months [1,2,3,4,5,6,7,8,9,10,11,12] and y is revenue [10200,10800,11500,12100,13400,14200,14900,15800,16500,17200,18100,19000]. Can you fit a linear regression (degree 1) to find the trend?"},{"title":"Curve fitting for physics experiment","prompt":"I've got experimental results from a projectile motion test — x positions are [0,1,2,3,4,5] meters and y heights are [0,4.9,9.6,14.1,18.4,22.5] meters. Fit a degree-2 polynomial regression to model the trajectory."},{"title":"Calibration curve for lab instrument","prompt":"I need a calibration curve for my spectrometer — my known concentrations are [0,0.5,1,2,5,10,20] and absorbance readings are [0,0.12,0.23,0.47,1.02,1.95,3.87]. Can you run a degree-1 regression to get the slope and intercept?"}],"resultDescription":"Returns a JSON object with a success flag, a human-readable computation description, and a result object containing regression coefficients (polynomial terms), and optionally goodness-of-fit metrics such as R-squared. The result varies by degree of polynomial requested.","failureModes":["x and y arrays of mismatched lengths return a validation error","empty or null arrays cause a 400-level error","non-numeric values in x or y arrays cause parsing failure","degree parameter set to 0 or negative may return an error or trivial result","very large arrays may cause timeout or memory issues on the serverless instance"],"whenToPreferThis":"Choose this endpoint when you need fast, serverless polynomial regression computation without setting up a local math library or calling a heavy data-science platform. Ideal for AI agents that need on-demand regression fitting within a workflow, especially when payment via x402 micropayments on Base is acceptable. Prefer alternatives if you need advanced statistical outputs (p-values, confidence intervals), very large datasets, or batch regression across many variable pairs.","instructions":null,"reviewSummary":null,"reviewSummaryHighlights":null,"reviewSummaryConcerns":null,"reviewSummaryGeneratedAt":null,"activationCount":0,"lastUsedAt":null,"lastSuccessfullyRanAt":null,"lastHealthCheckAt":"2026-09-14T12:52:04.113Z","isFirstParty":false}