Sample Reports - MCP Analytics

Linear Regression Analysis

Generated from the prompt: "Analyze the relationship between total bill and tip amount"

Model Performance

Actual vs Predicted Values

0.955

R-Squared

0.953

Adj R-Squared

4.57

RMSE

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Key Insights

Excellent Model Fit

R-squared of 0.955 indicates the model explains 95.5% of variance in the target variable—a strong predictive relationship.

Low Prediction Error

RMSE of 4.57 means predictions deviate from actual values by ~$4.57 on average. MAE of 3.69 confirms consistent accuracy.

Model Stability

Adjusted R-squared (0.953) closely matches R-squared, indicating the model isn't overfitting despite multiple predictors.

Next Steps

Investigate feature interactions and check for multicollinearity to further improve model reliability.

Anatomy of a Report

Every report includes comprehensive analysis components designed for real-world decision making

Interactive Visualizations

Zoom, pan, and hover over charts to explore your data. Residual plots, Q-Q plots, feature importance, time series decompositions, and correlation matrices.

Complete Metrics

R², RMSE, MAE, p-values, confidence intervals, AIC/BIC, and all relevant statistics for your analysis type. Nothing hidden.

AI-Generated Insights

Business-friendly interpretations that explain what the numbers mean. Key findings, recommendations, and actionable next steps.

Downloadable Data

Export predictions, residuals, coefficients, and processed datasets. Ready for further analysis or integration into your workflows.

Diagnostic Checks

Assumption validation, outlier detection, multicollinearity checks, and model diagnostics. Know if your results are trustworthy.

Full Methodology

Complete documentation of methods, parameters, and transformations. Reproducible analysis you can explain to stakeholders.

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mcp.reports.view

// Generate a shareable URL
mcp.reports.view(
  processing_id="mcp_linear_regression_abc123",
  expires_in=86400,  // 24 hours
  max_access_count=50
)

// Returns:
{
  "url": "https://api.mcpanalytics.ai/rpt/rpt_XYZ789",
  "expires_at": "2025-12-06T10:30:00Z",
  "remaining_views": 50
}

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Ask "What do we know about customer churn?" and get synthesized answers from every churn-related analysis you've ever run. Search by meaning, not just keywords.

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Find connections between analyses across different datasets, teams, and time periods. Your quarterly sales forecast connects to last year's seasonal analysis.

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                            $ "Find regression analyses with high R² from Q4"
                        
Found 4 matching reports:

                                1. Customer Churn Prediction (R²: 0.89)

                                2. Sales Forecast Model (R²: 0.92)

                                3. Revenue Driver Analysis (R²: 0.85)

                                4. Feature Importance Study (R²: 0.87)
                            
                            $ "Show me the first one"

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