Diagnose missing-data mechanisms and apply principled imputation strategies.
npx skills add https://github.com/sencersoylu/scholar-flow --skill missing-data
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
Missing data handling and principled imputation to enable valid analyses across datasets with incomplete records.
Assess the missing-data mechanism in your dataset (MCAR/MAR/MNAR) and apply an appropriate imputation method to prepare analysis-ready data.
High-intent search queries and answers about installing and using this skill.
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.
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.
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.
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.
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.
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