R Econometrics MCP Server

# Examples and Use Cases Comprehensive examples showing RMCP capabilities across different statistical analysis scenarios. ## Quick Start Examples For immediate hands-on experience, see: - [Quick Start Guide](../examples/quick_start_guide.md) - Basic usage examples - [Working Examples →](../examples/quick_start_guide.md) - Get started immediately ## Advanced Statistical Analysis ### Time Series Analysis [Advanced Time Series Example](../examples/advanced_time_series_example.md) - ARIMA modeling and forecasting - Seasonal decomposition - Stationarity testing - Economic forecasting workflows ### Economic Research [Economic Research Example](../examples/economic_research_example.md) - Panel data analysis - Instrumental variables - Policy impact analysis - Econometric modeling workflows ### Claude Desktop Integration [Claude Desktop Examples](../examples/claude_desktop_v0.5.0_examples.md) - Interactive statistical analysis - Real-time visualizations - Data exploration workflows - Advanced statistical conversations ## Statistical Categories ### Regression & Economics ```markdown "Analyze the relationship between marketing spend and ROI" "Run a panel data regression with fixed effects" "Test for endogeneity using instrumental variables" ``` ### Time Series & Forecasting ```markdown "Forecast next quarter's sales using ARIMA" "Decompose the time series into trend and seasonal components" "Test if the series is stationary" ``` ### Machine Learning ```markdown "Cluster customers by purchasing behavior" "Build a decision tree to predict churn" "What are the most important features?" ``` ### Statistical Testing ```markdown "Is my A/B test result statistically significant?" "Run an ANOVA to compare group means" "Test if the data follows a normal distribution" ``` ### Data Analysis & Visualization ```markdown "Summarize this dataset with descriptive statistics" "Create a correlation heatmap" "Generate a professional scatter plot" ``` ## Working with Data ### File Formats Supported - **CSV files**: `read_csv()`, `write_csv()` - **Excel files**: `read_excel()`, `write_excel()` - **JSON data**: `read_json()`, `write_json()` - **Direct data input**: Arrays, matrices ### Data Transformation ```markdown "Standardize the variables" "Create lag variables for time series" "Winsorize outliers at 5th and 95th percentiles" "Filter data where income > 50000" ``` ## Interactive Examples All examples are designed to work with: - **Claude Desktop** - Local MCP integration - **Claude Web** - HTTP server integration - **Direct API calls** - Programmatic access - **Jupyter notebooks** - Data science workflows ## Example Data Sets RMCP includes several example datasets for testing: - Economic indicators - Panel data - Time series data - Customer behavior data - Sales and marketing data Access via: `load_example("dataset_name")` ## Best Practices ### Effective Prompts - Be specific about the analysis you want - Mention the statistical method if you know it - Specify output format preferences - Ask for interpretations and insights ### Error Recovery - RMCP provides intelligent error diagnosis - Suggested fixes for common issues - Alternative approaches when methods fail - Data validation and cleaning suggestions ### Visualization - Plots are displayed inline in conversations - Professional styling with ggplot2 - Export capabilities (PNG, PDF, SVG) - Customizable themes and colors