Linear regression models are one of the most widely used statistical models in various fields such as finance, marketing, economics, and engineering. It is a simple model that assumes a linear relationship between the independent variable(s) and the dependent variable. Despite its popularity, linear models have some significant drawbacks that one should be aware of before using them. In this article, we will explore the drawbacks of a linear model and how they can affect the accuracy of the predictions.
Limited Flexibility:
The primary limitation of linear models is their inflexibility. Linear models assume a linear relationship between the dependent variable and independent variables. This assumption can be limiting, especially when the relationship between the variables is not linear. Nonlinear relationships are often more complicated and may require more complex models to capture their nuances.
Susceptible to Outliers:
Linear regression models are sensitive to outliers. An outlier is a data point that differs significantly from other data points. Outliers can influence the slope and intercept of the regression line, leading to inaccurate predictions. In extreme cases, outliers can even flip the sign of the regression…
