Handling Incomplete Data with Regression Imputation

Abstract

Regression analysis is widely used in various fields due to easy-to-understand. One of purposes of regression analysis is to predict the response variable using the predictor variables. Unfortunately, in real cases, some values may be missing. This circumstance will produce large error, indeed, poor prediction. Missing value lead us to trade-off remove the paired data points or replace. The purpose of this study was to estimate the missing value with regression imputation. This study conducted two scenarios of amount of missing values, 10% and 15%. The study results showed that the higher of amount of missing values, the higher the value of MSE was. The first section in your paper.