Correlation Analyzer
This Correlation Analyzer identifies relationships between variables, revealing hidden patterns and insights for data exploration and analysis.
Install on your platform
We auto-selected Claude Code based on this skill’s supported platforms.
Run in terminal (recommended)
claude mcp add correlation-analyzer npx -- -y @trustedskills/correlation-analyzer
Or manually add to ~/.claude/settings.json
{
"mcpServers": {
"correlation-analyzer": {
"command": "npx",
"args": [
"-y",
"@trustedskills/correlation-analyzer"
]
}
}
}Requires Claude Code (claude CLI). Run claude --version to verify your install.
About This Skill
What it does
The correlation-analyzer skill identifies and quantifies the statistical relationship between two datasets. It can determine if a positive, negative, or no correlation exists, along with a correlation coefficient value to indicate strength. This allows for data-driven insights into potential dependencies and patterns within datasets.
When to use it
- Market Research: Analyze the correlation between advertising spend and sales figures to optimize marketing campaigns.
- Scientific Studies: Determine if there's a relationship between different variables in an experiment, like temperature and reaction rate.
- Financial Analysis: Investigate correlations between stock prices or economic indicators to inform investment strategies.
- Customer Behavior: Identify relationships between website activity and purchase conversion rates.
Key capabilities
- Calculates correlation coefficients.
- Identifies positive and negative correlations.
- Quantifies the strength of correlations.
Example prompts
- "Analyze the correlation between these two datasets: [dataset 1] and [dataset 2]."
- "What is the correlation coefficient between temperature and ice cream sales?"
- "Is there a significant relationship between website visits and product purchases? Provide the correlation coefficient."
Tips & gotchas
The quality of the analysis depends heavily on the data provided; ensure datasets are clean, relevant, and appropriately formatted. Correlation does not equal causation - further investigation is often needed to understand underlying relationships.
Tags
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