I chose a linear regression model to analyze the impact of body-worn cameras (BWC) on the number of reported use-of-force incidents for several reasons. First, the dependent variable, which is the number of reported incidents, is continuous, making linear regression an appropriate choice for modeling such relationships (Cohen, 1988). I appreciate the simplicity and interpretability of linear regression, as the coefficients directly indicate how changes in the independent variable affect the dependent variable, which facilitates clear communication of the findings (Field, 2013). Additionally, this model allows me to include multiple control variables, such as crime rates and community demographics, helping to isolate the effect of BWCs while accounting for other influencing factors (Braga & Coldren, 2019). I also value that linear regression has well-established assumptionslike linearity and homoscedasticitythat can be tested, ensuring the robustness of my analysis (Field, 2013). Furthermore, previous research in criminal justice has successfully utilized this model to assess various interventions, including body-worn cameras, which supports its appropriateness for my study (Ariel, Farrar, & Sutherland, 2015; Lum, Koper, & Merola, 2015). Overall, I find that the linear regression model provides a solid framework for understanding the relationship between body-worn cameras and use-of-force incidents, while also allowing for potential extensions if needed