In the context of linear regression, what issue can outliers cause?

Question

BlackTom AI · Accepted Answer

Question restatement: In the context of linear regression, what issue can outliers cause?

Option 1: The model performs well on the training data but poorly on unseen data.
- This describes overfitting or poor generalization in a broad sense, not a direct consequence specific to outliers. Outliers can influence the fit, but this option attributes generalization issues to training data quality rather than the particular effect of outliers on the regression fit itself.

Option 2: The model’s predictions can be skewed, leading to an inaccurate fit.
- This is the core effect of outliers in ordinary least squares linear regression: a small number of extreme values can pull the regression line toward themselves, distorting predictions for the bulk of the data and producing a biased or inaccurate fit.
- The reasoning here hinges on the objective function (minimizing squared residuals), which is sensitive to large deviations; outliers exert disproportionate influence, shifting the line away from the true central tendency.

Option 3: The model is too simple and cannot capture the underlying trend.
- This describes underfitting or model misspecification where the chosen model is inadequate to represent the data, but it is not a direct description of the effect caused by outliers. Outliers affect the fit within the existing model structure rather than revealing that the model is too simple.

In summary, while overfitting, generalization, and underfitting are important concepts, the specific issue caused by outliers in linear regression is that they can skew the model’s predictions and produce an inaccurate fit, which aligns with Option 2.

In the context of linear regression, what issue can outliers cause?单项选择题

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