Missing Data & Application of M.I.C.E.
Missing data is a common challenge in data analysis, affecting everything from accuracy to interpretability. This presentation explores how missing data arises, why it matters, and practical strategies to handle it effectively.
Model: ChatGPT 4.5
I explored the impact of missing data patterns (MCAR, MAR, MNAR) on regression analysis using Multiple Imputation by Chained Equations (MICE), a method that leverages relationships among variables to fill gaps. My analysis showed MICE effectively preserved data integrity and provided unbiased parameter estimates, even when missingness reached 70%. Evaluation metrics like Percent Bias, Coverage Rate, and RMSE confirmed MICE’s statistical validity, highlighting its advantage over simpler imputation techniques.