Does this make sense "For my thesis, we are considering a regression model due to its compatibility with a continuous dependent variable, in this case, 'housing instability.' This model will have a blend of both continuous and categorical independent variables. These variables include but are not limited to, mental health factors, such as depression and anxiety; substance use variables, representing both the extent and the impact of use; and various demographic factors (Age, Gender, Race, and SES, which provide a rich context to our study. Why a regression model? Its utility lies in its capacity to quantify the strength and direction of the relationship between our chosen predictors and our primary outcome of interest - housing instability. Through this model, we aim to see which factors hold the most weight in influencing housing outcomes for individuals with TBI. The beauty of this approach is in its intricacy and depth. With each variable we introduce into our model, we peel back another layer of the complex narrative that underpins housing instability among the TBI-affected populace. In essence, the regression model is not just a statistical tool. It can shed light on the intricacies of causality and correlation, guiding us toward evidence-based solutions to mitigate the risk of housing instability for TBI survivors