Regression analysis

exploratory statistics

slug: recipe-exploratory-statistics-regression-analysis

Recipe: Regression analysis

category: exploratory statistics

Problem

You need to model relationships between variables:

• predict numeric outcomes based on one or more predictors
• quantify the strength and significance of relationships
• identify key drivers of observed patterns

Solution

Follow these steps to perform regression analysis:

• load the dataset
• select dependent and independent variables
• apply regression analysis (linear, multiple, or custom)
• review coefficients, p-values, and model diagnostics
• optionally use predictions for downstream analysis

Step Sequence

load step -> [step-reg] -> calculate step -> chart step

Input Datasets

• transactions_clean — cleaned transactional data
• Notes: numeric dependent variable (e.g., total_sales) and predictor variables (e.g., quantity, discount, customer_segment encoded numerically)

Output Dataset

• regression_results — table containing coefficients, standard errors, p-values, and optionally fitted values
• Notes: can be used to interpret drivers or generate predictions

Step-By-Step Explanation

Step	Purpose	Notes
load step	Load dataset	Supports local file, database, or API sources
[step-reg]	Fit regression model	Example: linear regression of total_sales ~ quantity + discount
calculate step	Compute derived metrics or predictions	Optional: create predicted sales or residuals
chart step	Visualize relationships or residuals	Optional scatterplots, regression lines, or residual plots

Variations & Extensions

• Perform multiple regression with multiple independent variables
• Use filter step to subset dataset before regression
• Combine with [step-corr] to check predictor multicollinearity
• Include dashboard step to display results interactively

Concepts Demonstrated

• Regression modeling
• Predictor and outcome analysis
• Model diagnostics and interpretation
• Sequencing analysis and visualization steps

Related Recipes

• Univariate analysis of numeric variables
• Correlation analysis between numeric variables

Notes & Best Practices

• Check assumptions: linearity, independence, normality of residuals
• Document variables and transformations applied
• Use visualization to confirm model fit and identify outliers

Metadata

title: "Regression analysis"
category: "exploratory statistics"
difficulty: "Intermediate"
tags: [regression, modeling, numeric, EDA]
inputs: [transactions_clean]
outputs: [regression_results]
steps: [step-load, step-reg, step-calculate, step-chart]
author: "Tom Argiro"
last_updated: "2025-10-25"
doc_type: "recipe"