Cox Regression
Cox regression, also known as the Cox proportional hazards model, is a statistical method used in survival analysis to assess the effect of multiple variables on the time until an event occurs, such as death or failure. It models the hazard rate (instantaneous risk of the event) as a function of covariates, without requiring a specific distribution for the survival times. This semi-parametric approach makes it widely applicable in fields like medical research, engineering reliability, and social sciences.
Developers should learn Cox regression when working on data science or machine learning projects involving time-to-event data, such as predicting customer churn, equipment failure, or patient survival in healthcare analytics. It is particularly useful for handling censored data (where some subjects haven't experienced the event by the study's end) and for identifying risk factors that influence event timing, enabling more accurate predictive models in applications like clinical trials or predictive maintenance.