In the network technology era, the collected data are growing more and more complex, and become larger than before. It brings the difficulty to analyze by using the standard statistical tools. In this thesis, we focus on estimates of the linear regression parameters for symbolic interval data. We propose two approaches to estimate regression parameters for symbolic interval data under two different data models and compare our proposed approaches with the existing methods via simulations. Finally, we analyze two real datasets with the proposed methods for illustrations.