KiHustle Box Plot Statistics Calculator

x402

KiHustle Box Plot Statistics Calculator is a paid API for AI agents from kihustle.tech, paid per call via x402, $0.002/call, status unknown (last checked 2026-11-01).

Computes box plot statistics (quartiles, median, min, max, outliers) from an array of numeric values

Price / call$0.002

Seller volume →—

Success—

StatusUnknown

How this service works

Kostenlose Guides, Solo-Playbooks und Artikel zu KI, Automation und Side Hustles — für Menschen, die mit echten Systemen online Einkommen aufbauen wollen. Transparent finanziert über faire Affiliate-Links.

Output

Returns a JSON object containing box plot statistics derived from the input numeric array, including minimum, maximum, median (Q2), lower quartile (Q1), upper quartile (Q3), interquartile range (IQR), and optionally identified outlier values.

Example use cases

Data distribution analysis for reporting

“I have a list of monthly revenue figures — [1200, 1350, 980, 2100, 1450, 870, 3200, 1300, 1550, 1400] — can you compute the box plot stats so I can understand the distribution and spot any anomalies?”

Outlier detection in sensor readings

“Here are temperature readings from our IoT sensors this week: [22.1, 22.5, 23.0, 21.8, 38.4, 22.3, 22.9, 23.1, 21.5, 22.7] — calculate the box plot statistics so I can identify which readings look like outliers.”

Student grade distribution summary

“I have a class of students with these test scores: [55, 60, 62, 70, 72, 75, 78, 80, 85, 88, 91, 95, 98] — can you run box plot stats so I can see the median, quartiles, and whether any scores are unusually high or low?”

Technical details

Endpoint

POSThttps://kihustle.tech/api/v1/json-box-plot-stats?utm_source=zero.xyz

When to prefer

Choose this endpoint when you need fast, lightweight box plot statistics computed server-side from a raw numeric array, especially within a micropayment-per-call workflow where you want to avoid spinning up a local statistics library. Suitable for one-off computations on small to medium datasets without needing a full analytics platform.

Side effects

Empty array input returns error or undefined statistics
Non-numeric values in array cause validation failure
Very large arrays may time out
Single-element arrays produce degenerate statistics with identical Q1/Q2/Q3
Malformed JSON body returns 400 error

Input

Schema · Request body

Field	Type	Description
values	array

Output

Schema · Response

{
  "type": "json",
  "example": {
    "result": "processed",
    "status": "success"
  }
}

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