Abstract
The problem of missing data is a major obstacle for researchers in the process of data analysis in various fields, and this problem appears frequently in all fields of social, medical, astronomical studies, clinical trials, and others. The presence of such a problem within the data to be studied will negatively affect its analysis and then lead to misleading conclusions, and these conclusions result from the great bias caused by this problem. Therefore, this work provides a comprehensive analysis of the different methods used to solve the problem of missing data in databases. It identifies the different types of missing data and points out the most common types of regression analysis. It also aims to introduce the reader to many methods for solving the problem of missing data in regression analysis, while explaining how these methods affect the final conclusions of the study. Paper type: Promotion paper
