Linear regression only works under four key assumptions, summarized by the acronym L.I.N.E.: Linearity, Independence, Normality, and Equal variance (homoscedasticity). Violating any of these can make an otherwise accurate-seeming model produce misleading predictions and unreliable statistics. This is analogous to a GPS that only works on straight, flat roads—its performance collapses in real-world complexity.

The article uses a practical GPS analogy to illustrate each violation. For instance, testing a model only on highway data (straight lines) ignores mountain curves, leading to a linearity violation. Similarly, time-series data violates independence as each point depends on the last. Recognizing these issues requires diagnostic tools like residual plots and statistical tests such as the Durbin-Watson test.

When assumptions fail, solutions exist. You can transform variables (e.g., using log or polynomial features), apply robust regression methods, or switch to models that don't require these assumptions, like tree-based ensembles. The core takeaway is that checking these assumptions is a mandatory step before trusting any linear model's output, ensuring your predictions hold up beyond controlled test environments.