The process of regression imputation involves using a statistical model to estimate the missing values. Typically, a regression model is created using the observed data, where the dependent variable is the one with missing values, and independent variables are those that are fully observed. The model then generates predictions for the missing values based on the relationships identified in the complete cases. This approach assumes that the data are missing at random (MAR), meaning that the missingness is related to the observed data but not the missing data itself.
