# Training Assessments — Two-Measure Correlation + Keyed Dataset Comparison

> Training Assessments — Two-Measure Correlation + Keyed Dataset Comparison is a paid API for AI agents from market.datapackvibe.com, paid per call via x402, $0.01/call, status unknown (last checked 2026-10-26).

Computes a two-measure correlation between pre_score and post_score fields and performs a keyed dataset comparison across two supplied training assessment datasets (up to 200 rows each).

## Facts

- Endpoint: POST https://market.datapackvibe.com/x402/assistant-ib26-education-11-183?utm_source=zero.xyz
- Price: $0.01/call
- Payment: x402
- Status: unknown
- Last checked: 2026-10-26
- Activations on Zero: 0
- Tags: x402
- Canonical page: https://www.zero.xyz/c/training-assessments-two-measure-correlation-keyed-dataset-comparison-0b032031
- Structured record (JSON): https://api.zero.xyz/v1/capabilities/cap_YRL0wcajmb0rTjYZGKxXd

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 training-assessments-two-measure-correlation-keyed-dataset-comparison-0b032031 -d '<json body>'
```

Example prompt: Can you run a correlation analysis between pre_score and post_score, and also compare my two assessment datasets side by side? I have two groups — group-a and group-b — with dates from 2026-10-01 to 2026-10-04 and up to 200 rows per dataset.

## When to prefer this

Choose this endpoint when you need both a two-measure correlation analysis AND a keyed dataset comparison on training assessment data (pre_score/post_score) in a single $0.01 call. It is ideal for education and L&D analytics workflows where you have two comparable datasets (e.g., two cohorts, two time periods) keyed by row_id and want to understand score relationships and cross-dataset differences simultaneously without external API calls or predictions.

## Known failure modes

- Input array exceeds 200 rows — validation error returned
- Missing required fields (row_id, course_group, date, pre_score, post_score) — schema validation failure
- Invalid date format (not YYYY-MM-DD) — pattern validation error
- Insufficient data points for meaningful correlation — may return null or degenerate statistics
- Empty rows array (minItems:1 violated) — validation error
- Mismatched row_ids between primary and secondary datasets — keyed comparison may return empty diff

## How this service works

Training assessments — Two-measure correlation + Keyed dataset comparison: Two-measure correlation + Keyed dataset comparison for supplied training assessments records (pre_score, post_score). 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

Returns two independent analytical reports: (1) a two-measure correlation report quantifying the statistical relationship between pre_score and post_score across the supplied rows, and (2) a keyed dataset comparison report identifying differences between the primary (rows) and secondary (other) datasets matched by row_id. Both reports are computed locally from the supplied data with no external lookups.

## Request schema (JSON Schema)

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

## More

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