Model breakdown

Definition

Model breakdown occurs when a linear model no longer accurately represents the data due to assumptions being violated, such as non-linearity or outliers. This leads to incorrect predictions and interpretations.

5 Must Know Facts For Your Next Test

1. Model breakdown can occur due to non-linearity in the data.
2. Outliers can cause a linear model to break down by disproportionately influencing the slope.
3. Heteroscedasticity, where the variability of errors is not constant, contributes to model breakdown.
4. Multicollinearity among predictors can lead to unstable estimates and model breakdown.
5. Residual analysis helps detect signs of model breakdown such as patterns or large residuals.

Review Questions

• What are two common causes of model breakdown?
• How can you use residual analysis to identify a potential model breakdown?
• Why does multicollinearity lead to a breakdown in linear models?

Related terms

Linear Regression: A statistical method for modeling the relationship between a dependent variable and one or more independent variables using a linear equation.

Outlier: An observation that lies an abnormal distance from other values in a dataset, potentially skewing results.

Residuals: The differences between observed values and predicted values in a regression model, used to assess fit.