# Numeric Precision Comparisons — Grouped Summary + IQR Outlier Flags

> Numeric Precision Comparisons — Grouped Summary + IQR Outlier Flags is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-23).

Computes grouped numeric summary statistics and IQR-based outlier flags for source_value vs transformed_value pairs, covering up to 200 rows across two independent reports.

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-analytics-20-094?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-23
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/numeric-precision-comparisons-grouped-summary-iqr-outlier-flags-46b868f0
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap___63rN1b2r5DkuMEH8dmS

Status and success rate cover calls made through Zero and Zero's own probes. Third-party monitors may report differently.

## How to call it through Zero

Zero handles the 402 payment challenge and records the run. With the Zero CLI installed (`npm i -g @zeroxyz/cli`):

```sh
zero fetch --capability numeric-precision-comparisons-grouped-summary-iqr-outlier-flags-46b868f0 -d '<json body>'
```

Example prompt: Can you analyze these 150 rows of source vs transformed numeric values grouped by department — I want a grouped summary and IQR outlier flags to see which rows look suspicious after the transformation?

## When to prefer this

Choose this endpoint when you need both grouped descriptive statistics and IQR outlier detection on paired numeric records (source vs transformed) in a single call, without needing external lookups, ML predictions, or live data. It is ideal for data quality audits, ETL validation, and transformation accuracy checks on batches up to 200 rows, especially when cost efficiency ($0.01 for 2 reports) matters.

## Known failure modes

- Fewer than 1 row provided — rejected by minItems constraint
- More than 200 rows submitted — exceeds maxItems limit
- Invalid date format (not YYYY-MM-DD) — schema validation failure
- source_value or transformed_value outside ±1,000,000,000,000 range — rejected
- row_id missing or empty — required field violation
- Insufficient data within a group to compute IQR meaningfully — may produce null or degenerate statistics

## How this service works

Numeric precision comparisons — Grouped numeric summary + IQR outlier flags: Grouped numeric summary + IQR outlier flags for supplied numeric precision comparisons records (source_value, transformed_value). 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.

## Output

Two independent analytical reports: (1) a grouped numeric summary (e.g. mean, median, std dev, min/max) broken down by dataset_group for the selected field, and (2) IQR-based outlier flags identifying rows whose values fall beyond 1.5× the interquartile range, covering up to 200 input rows.

## Request schema (JSON Schema)

```json
{
 "type": "object",
 "properties": {
  "rows": {
   "type": "array",
   "items": {
    "type": "object",
    "required": [
     "row_id",
     "dataset_group",
     "date",
     "source_value",
     "transformed_value"
    ],
    "properties": {
     "date": {
      "type": "string",
      "default": "",
      "pattern": "^\\d{4}-\\d{2}-\\d{2}$",
      "maxLength": 10
     },
     "row_id": {
      "type": "string",
      "default": "",
      "maxLength": 80,
      "minLength": 1
     },
     "source_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     },
     "dataset_group": {
      "type": "string",
      "default": "",
      "maxLength": 80
     },
     "transformed_value": {
      "type": [
       "number",
       "null"
      ],
      "maximum": 1000000000000,
      "minimum": -1000000000000
     }
    },
    "additionalProperties": false
   },
   "default": [
    {
     "date": "2026-10-01",
     "row_id": "row-1",
     "source_value": 10,
     "dataset_group": "group-a",
     "transformed_value": 1
    },
    {
     "date": "2026-10-02",
     "row_id": "row-2",
     "source_value": 20,
     "dataset_group": "group-b",
     "transformed_value": 2
    },
    {
     "date": "2026-10-03",
     "row_id": "row-3",
     "source_value": 15,
     "dataset_group": "group-a",
     "transformed_value": 3
    },
    {
     "date": "2026-10-04",
     "row_id": "row-4",
     "source_value": 30,
     "dataset_group": "group-b",
     "transformed_value": 4
    }
   ],
   "maxItems": 200,
   "minItems": 1
  },
  "field": {
   "enum": [
    "source_value",
    "transformed_value"
   ],
   "type": "string",
   "default": "source_value"
  }
 }
}
```

## More

- Live health (JSON, refreshed every minute): https://www.zero.xyz/c/numeric-precision-comparisons-grouped-summary-iqr-outlier-flags-46b868f0/health.json
- [Zero catalog index](https://www.zero.xyz/llms.txt)
- [Other services from market.datapackvibe.com](https://www.zero.xyz/host/market.datapackvibe.com/llms.txt)