ACCA Performance Management (F5) Certification Practice Exam

What is a primary limitation of linear regression?

1. It requires non-linear data.

2. It assumes a linear relationship between variables.

3. It can analyze more than two variables.

4. It is effective for all forecasting scenarios.

The correct answer is: It assumes a linear relationship between variables.

The primary limitation of linear regression is that it assumes a linear relationship between the independent and dependent variables. This means that linear regression can only model situations where the change in the dependent variable is proportional to the change in the independent variable. When the actual relationship is nonlinear, using linear regression can lead to inaccurate predictions and misleading conclusions. For example, if the true relationship between variables is quadratic or exponential, a linear model will not fit the data well, leading to poor performance when making predictions. Therefore, it is crucial to assess the nature of the relationship between variables before applying linear regression, as failing to do so can compromise the results. Other options highlight different aspects of regression analysis. Some might focus on the capacity of linear regression to include multiple variables or suggest that it can apply to various forecasting scenarios, which do not directly address the inherent limitation regarding the assumption of linearity in relationships.