Model breakdown

Definition

Model breakdown occurs when a linear model no longer accurately represents the data due to factors like outliers, non-linearity, or changes in trends. It indicates that the assumptions of a linear relationship are violated.

5 Must Know Facts For Your Next Test

1. Model breakdown can be detected through residual plots showing patterns rather than randomness.
2. Outliers and high leverage points can cause a model breakdown by disproportionately influencing the linear fit.
3. Non-linear relationships in data can lead to model breakdown as linear models assume constant rate of change.
4. Changes in trends over time or different segments of data may indicate different underlying relationships, leading to model breakdown.
5. Addressing model breakdown might involve transforming variables, removing outliers, or using more complex models.

Review Questions

• What are common indicators of a model breakdown in residual plots?
• How do outliers contribute to the breakdown of a linear model?
• What steps can be taken if a linear model breaks down due to non-linear relationships?

Related terms

Residual Plot: A graph that displays the residuals on the vertical axis and the independent variable on the horizontal axis. It helps diagnose issues with fitting a linear model.

Outlier: A data point significantly different from others in a dataset. It can distort statistical analyses and affect linear models.

Non-linearity: A relationship between variables that cannot be accurately described with a straight line. Non-linear patterns require more complex modeling techniques.