Normal probability plot

Open Normal probability plot on Happytails.ai. This view has the same page content and links, with explicit tool capabilities. Read the processing notes before using a tool.

Tool capabilities

{
  "id": "normal-probability-plot",
  "category": "calculators",
  "built": true,
  "requirements": "Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population. Search and sort change this comparison only. Copy result copies the summary. Download JSON or CSV for all observations, results, settings and method assumptions. Download SVG for the chart. One disposable worker per calculation, with a 10-second timeout. CSV supports up to 100 columns and 100000 cells through the shared parser.",
  "implementation": {
    "fields": [
      {
        "key": "reference",
        "label": "Normal reference",
        "value": "fitted",
        "type": "select",
        "values": [
          "fitted",
          "specified"
        ],
        "labels": {
          "fitted": "Fit sample mean and SD",
          "specified": "Use specified mean and SD"
        }
      },
      {
        "key": "mean",
        "label": "Reference mean",
        "value": "0",
        "type": "text",
        "inputMode": "decimal",
        "maxLength": 24,
        "showWhen": {
          "reference": [
            "specified"
          ]
        }
      },
      {
        "key": "sd",
        "label": "Reference standard deviation",
        "value": "1",
        "type": "text",
        "inputMode": "decimal",
        "maxLength": 24,
        "showWhen": {
          "reference": [
            "specified"
          ]
        }
      }
    ],
    "presentation": "metrics",
    "inputMaxLength": 1000000,
    "inputErrorCode": "DIAGNOSTIC_INPUT",
    "note": "Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population.",
    "experience": {
      "automatic": false,
      "single": false,
      "inputMaxLength": 1000000,
      "inputMode": "text",
      "label": "Numeric CSV",
      "placeholder": "value\n1\n2\n3",
      "help": "Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population. Up to 1,000,000 characters. Review the settings before running.",
      "helpParts": [
        "Paste comma-separated CSV with a header and 2 to 2000 complete rows. Use dot decimals with at most six decimal places and values from -1000000000 to 1000000000. No missing values are silently dropped. Extra columns are used only to preserve the original input in reports. Calculations use the required columns only. Processing stays on your device. Results use floating-point arithmetic. Charts show the supplied sample, not proof about a population. Use a value column. Compare observations with a normal reference fitted from sample mean and sample SD, or a normal reference with a specified mean from -1000000000 to 1000000000 and SD from 0.000001 to 1000000000. Plotting positions are Hazen (rank-0.5)/n. A fitted reference uses the same data and is not an independent validation. A constant sample has zero fitted spread. The plot is a visual diagnostic, not a normality test, p-value or proof of a normal population.",
        "Up to 1,000,000 characters.",
        "Review the settings before running."
      ],
      "action": null
    },
    "mode": "explicit",
    "interactive": false
  },
  "execution": "Browser interface",
  "api": null,
  "browser_agent": {
    "supported": true,
    "name": "run_current_tool",
    "discovery": "WebMCP on the human page in a supporting browser",
    "verification": "See audit/API-AUDIT.md; availability is not verification"
  }
}

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