Multiple Regression Model Assumptions Violation

Consider a multiple regression model designed to predict the annual salary of employees based on their years of experience, education level, and age. The model can be expressed as:

$$ ext{Salary} = \beta_0 + \beta_1 \times \text{Experience} + \beta_2 \times \text{Education} + \beta_3 \times \text{Age} + \epsilon $$

Where:

• $$ \beta_0 $$: intercept
• $$ \beta_1, \beta_2, \beta_3 $$: coefficients of the predictors
• $$ \epsilon $$: error term

The researchers conducting this analysis assume the following characteristics about the error term $$ \epsilon $$:

• Independence of residuals
• Homoscedasticity (constant variance of error terms)
• Normal distribution of the residuals

Which of the following statements best describes a violation of these regression assumptions?