Calendering is a key yet complex manufacturing process that has varied effects on the Li-ion battery cell performance. Finding the optimal compaction can require many experiments if using the traditional one-factor-at-a-time method, which would be both complex and resource intensive. Design of Experiments (DoE) coupled with modeling via multiple linear regression (MLR) are used in this study to better understand the complex process of electrode calendering. The factors studied in this report are rolling temperature, post-calendered porosity, and mass loading of NMC622 and their effect on the electrochemical performance. The exact combinations of these factors which will create an information-rich data set are prepared using DoE. MLR is then used to extract this information in the form of understanding how each factor (individually and in combination with other ones) affect the electrochemical performance of the cells. Via this approach, the physical and statistical significance of each factor is quantified. Furthermore, the models are used to determine which combinations of factors result in an optimal electrochemical performance. Overall, this study highlights the use of both DoE to highlight the most important regions in the design space to conduct experiments while MLR is used to model and understand the complex system (calendering).