What is a limitation of using linear regression?

Using linear regression indeed has a key limitation in that it assumes a linear relationship between the two variables involved. This means that linear regression is best suited for data that follows a straight-line correlation. If the actual relationship between the variables is nonlinear, applying linear regression can lead to poor model performance and inaccurate predictions.

When the true relationship is not linear, the assumptions of the linear model do not hold true, which can result in residuals that are not randomly distributed, potential mis-estimation of the coefficients, and ultimately, a model that does not fit the data well. Therefore, acknowledging this assumption is crucial for accurately interpreting the results and ensuring that any predictions made are reliable.

In contrast, other options present characteristics or outcomes that do not accurately describe the limitations of linear regression. For example, it does not work well for all types of data, does not guarantee accurate predictions for future outcomes, and while it may indeed be quick and easy to implement, the ease of use does not mitigate the limitations inherent in its assumptions.