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Data Analysis

Regression Analyzer

Advanced regression analysis tool for performing linear regression analysis, calculating regression statistics, and making predictions. Perfect for statistical modeling, trend analysis, forecasting, and understanding relationships between variables. Features: - Simple linear regression (y = mx + b) - Multiple linear regression support - Regression coefficients calculation - Statistical significance testing - R-squared and adjusted R-squared - Residual analysis and diagnostics - Prediction intervals and confidence intervals - Outlier detection in regression - Model validation metrics - Visual regression diagnostics - Data transformation support Common Use Cases: - Sales forecasting and trend analysis - Financial modeling and risk assessment - Scientific research and hypothesis testing - Quality control and process optimization - Marketing analytics and ROI analysis - Medical and biological research

text· Text resultOpen tool

Quick start

Call this tool from your code in three languages.

cURL
curl -X POST 'https://api.elysiatools.com/en/api/tools/regression-analyzer' \
  -H 'Content-Type: application/json' \
  -d '{"inputData":"sales,advertising,price,season\n1500,200,29.99,1\n1800,250,27.99,2\n2100,300,24.99,1\n1900,280,26.99,2\n2400,350,22.99,1","targetColumn":"sales","featureColumns":"advertising, price, season\n\nLeave empty to auto-detect numeric columns","regressionType":"multiple","confidenceLevel":"0.95","handleMissing":"remove","outlierMethod":"none","includeIntercept":true,"standardizeFeatures":false,"generatePredictions":false,"predictionData":"advertising, price, season\n320,21.99,1\n280,25.99,2","includeDiagnostics":true,"decimalPlaces":4}'

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API reference

Send a POST request with your inputs as JSON. File parameters require a separate upload first.

Endpoint

HTTP
POST https://api.elysiatools.com/en/api/tools/regression-analyzer

Parameters

NameTypeRequiredDescription
inputDatatextareaYes—
targetColumntextYes—
featureColumnstextareaNo—
regressionTypeselectNo—
confidenceLevelselectNo—
handleMissingselectNo—
outlierMethodselectNo—
includeInterceptcheckboxNo—
standardizeFeaturescheckboxNo—
generatePredictionscheckboxNo—
predictionDatatextareaNoCSV data for making predictions (must include same feature columns as training data)
includeDiagnosticscheckboxNo—
decimalPlacesnumberNoNumber of decimal places for regression coefficients and statistics

Response format

Text result

JSON
{
  "result": "Processed text content",
  "error": "Error message (optional)",
  "message": "Notification message (optional)",
  "metadata": {
    "key": "value"
  }
}

MCP integration

Add this tool to your Model Context Protocol server so AI agents can list and call it.

Server configuration

Add this block to your MCP client configuration:

mcp.json
{
  "mcpServers": {
    "elysiatools-regression-analyzer": {
      "name": "regression-analyzer",
      "description": "Advanced regression analysis tool for performing linear regression analysis, calculating regression statistics, and making predictions. Perfect for statistical modeling, trend analysis, forecasting, and understanding relationships between variables.\n\nFeatures:\n- Simple linear regression (y = mx + b)\n- Multiple linear regression support\n- Regression coefficients calculation\n- Statistical significance testing\n- R-squared and adjusted R-squared\n- Residual analysis and diagnostics\n- Prediction intervals and confidence intervals\n- Outlier detection in regression\n- Model validation metrics\n- Visual regression diagnostics\n- Data transformation support\n\nCommon Use Cases:\n- Sales forecasting and trend analysis\n- Financial modeling and risk assessment\n- Scientific research and hypothesis testing\n- Quality control and process optimization\n- Marketing analytics and ROI analysis\n- Medical and biological research",
      "baseUrl": "https://api.elysiatools.com/mcp/sse?toolId=regression-analyzer",
      "command": "",
      "args": [],
      "env": {},
      "isActive": true,
      "type": "sse"
    }
  }
}

List available tools

After connecting to the SSE endpoint, list the exposed tools:

tools/list
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/list"
}

Call this tool

Invoke the tool by its id, passing arguments built from its parameters:

tools/call
{
  "jsonrpc": "2.0",
  "id": 2,
  "method": "tools/call",
  "params": {
    "name": "regression-analyzer",
    "arguments": {
  "inputData": "sales,advertising,price,season\n1500,200,29.99,1\n1800,250,27.99,2\n2100,300,24.99,1\n1900,280,26.99,2\n2400,350,22.99,1",
  "targetColumn": "sales",
  "featureColumns": "advertising, price, season\n\nLeave empty to auto-detect numeric columns",
  "regressionType": "multiple",
  "confidenceLevel": "0.95",
  "handleMissing": "remove",
  "outlierMethod": "none",
  "includeIntercept": true,
  "standardizeFeatures": false,
  "generatePredictions": false,
  "predictionData": "advertising, price, season\n320,21.99,1\n280,25.99,2",
  "includeDiagnostics": true,
  "decimalPlaces": 4
}
  }
}
Chain multiple tools in one session with a comma-separated toolId list, e.g. /mcp/sse?toolId=png-to-webp,jpg-to-webp,gif-to-webp (max 20).

Questions or issues? Contact [email protected]

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