missing-data

Diagnose missing-data mechanisms and apply principled imputation strategies.

Updated Mar 19, 2026

Search Tags:#data-cleaning #imputation #statistical-analysis #missing-data #mcar #mar #mnar

Ready to run

npx skills add https://github.com/sencersoylu/scholar-flow --skill missing-data

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: missing-data
Source: https://github.com/sencersoylu/scholar-flow/tree/main/skills/statistics/missing-data
Command: npx skills add https://github.com/sencersoylu/scholar-flow --skill missing-data

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Missing data handling and principled imputation to enable valid analyses across datasets with incomplete records.

Core Features & Use Cases

Detect missing data patterns (MCAR/MAR/MNAR) and assess their impact
Apply imputation strategies (mean/median/mode, regression-based, multiple imputation)
Integrate into statistical analyses, ML pipelines, and data cleaning workflows
Use Case: Prepare datasets with missing values for regression or machine learning models

Quick Start

Assess the missing-data mechanism in your dataset (MCAR/MAR/MNAR) and apply an appropriate imputation method to prepare analysis-ready data.

Frequently Asked Questions about missing-data

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I identify missing data mechanisms like MCAR, MAR, and MNAR in my dataset?▼

To identify missing data mechanisms, diagnose whether values are missing completely at random (MCAR), missing at random (MAR), or missing not at random (MNAR). This Skill assesses these patterns to determine their impact and guide appropriate imputation strategies for valid statistical analyses.

What is the best way to impute missing values for machine learning pipelines?▼

Imputing missing values for machine learning pipelines is best handled through principled strategies like mean/median/mode, regression-based, or multiple imputation. This Skill applies these methods to prepare analysis-ready data for regression and machine learning models.

How do I prepare a dataset with incomplete records for statistical analysis?▼

Preparing a dataset with incomplete records for statistical analysis involves detecting missing patterns and applying imputation. This Skill integrates into data cleaning workflows to handle missing data, enabling valid analyses across datasets with incomplete records.

Can I use multiple imputation to handle missing data in regression models?▼

Yes, you can use multiple imputation to handle missing data in regression models. This Skill supports multiple imputation strategies alongside mean, median, mode, and regression-based methods, drawing on documented references to Rubin (1976) and van Buuren (2018) for principled data preparation.

When should I not use mean imputation for missing data cleaning?▼

Mean imputation for missing data cleaning should be avoided when the missing data mechanism is not completely at random, as it can distort variance and relationships. This Skill helps by first diagnosing MCAR, MAR, and MNAR patterns to select a statistically valid imputation method.