Correlation analysis between numeric variables

exploratory statistics

slug: recipe-exploratory-statistics-correlation-analysis-between-numeric-variables

Recipe: Correlation analysis between numeric variables

category: exploratory statistics

Problem

You need to understand relationships between numeric variables:

• identify positive or negative correlations
• detect multicollinearity for modeling
• uncover patterns that inform further analysis

Solution

Follow these steps to compute and interpret correlations:

• load the dataset
• select numeric fields of interest
• compute pairwise correlations
• optionally visualize correlations for easier interpretation

Step Sequence

load step -> [step-corr] -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• Notes: numeric fields such as amount, quantity, discount, etc.

Output Dataset

• correlation_matrix — table showing correlation coefficients between selected variables
• Notes: can be used to detect strong relationships or potential multicollinearity

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load dataset	Supports local file, database, or API sources
[step-corr]	Compute pairwise correlations	Example: Pearson or Spearman correlations between numeric fields
chart step	Visualize correlation matrix	Optional heatmap or scatter plot for interpretation

Variations & Extensions

• Filter dataset using filter step to analyze subsets
• Combine with calculate step to create derived metrics before correlation
• Use [step-rank] to examine ranked relationships between variables

Concepts Demonstrated

• Pairwise correlation analysis
• Identifying relationships between numeric variables
• Detecting multicollinearity
• Sequencing statistics and visualization steps

Related Recipes

• Univariate analysis of numeric variables
• Ranking variables or observations using [step-rank]

Notes & Best Practices

• Inspect correlations visually to detect non-linear relationships
• Document which variables were analyzed and method used
• Consider removing or transforming highly correlated variables for modeling

Metadata

title: "Correlation analysis between numeric variables"
category: "exploratory statistics"
difficulty: "Intermediate"
tags: [correlation, numeric, EDA]
inputs: [transactions_clean]
outputs: [correlation_matrix]
steps: [step-load, step-corr, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"