regressionimputation

Regression imputation is a statistical method used to address missing data within a dataset by estimating and replacing absent values with predicted ones based on observed data. This technique leverages the relationship between the variable with missing values and other variables in the dataset to generate plausible estimates, thereby enabling more complete and accurate analyses.

The process begins with identifying the variable that has missing data and selecting predictor variables that are correlated with it. A regression model, commonly linear regression, is then fitted using cases where data are complete. This model captures the relationship between the predictor variables and the target variable. Missing values are subsequently imputed by applying this model to cases with incomplete data, using the known predictor values to predict the missing entries.

Regression imputation offers advantages such as preserving relationships among variables and maintaining the statistical properties of the original data. It is particularly useful when the data are missing at random (MAR) or missing completely at random (MCAR). However, it can underestimate variability because it imputes single predicted values, failing to account for uncertainty inherent in the estimation process.

To address this limitation, multiple imputation techniques extend regression imputation by creating several different plausible datasets, allowing for more robust statistical inference. Overall, regression imputation is a widely used and straightforward method for handling missing data, especially when relationships between variables are well-understood and the assumption of data missingness is reasonable.