Linear regression is a fundamental statistical method used to model the relationship between a dependent variable and one or more independent variables. The post discusses how the method uses the least squares approach to find the best-fitting line through the data, minimizing the sum of the squared differences between observed values and predicted values. A key point made in the user comments is about understanding the conditions under which the derivative indicates a minimum through the concept of the second derivative (Hessian in higher dimensions), which is critical for identifying stationary points. Additionally, the transformation of data problems to geometric visualizations enhances understanding and solution-finding in linear regression problems. This geometric perspective offers powerful insights into the optimization process, particularly in higher-dimensional spaces.