What is regression imputation and how is it used in data analysis?

What is regression imputation and how is it used in data analysis?

Regression imputation is a technique used to estimate missing values in a dataset by predicting them based on other available data. It uses regression models to fill in the gaps.

Can you explain how to perform regression imputation step by step?

Sure! First, identify the variable with missing values. Then, select predictor variables that are correlated with it. Next, build a regression model using the complete cases. Finally, use this model to predict and fill in the missing values.

What are the advantages and disadvantages of using regression imputation?

The advantages include maintaining the dataset size and leveraging relationships between variables. However, disadvantages include the risk of introducing bias and underestimating variability.

Are there any alternatives to regression imputation?

Yes, alternatives include mean/mode imputation, k-nearest neighbors, and multiple imputation, each with its own pros and cons.