Linear regression is easily distorted by outliers, missing data, and measurement errors. Which term describes this data sensitivity?

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Multiple Choice

Linear regression is easily distorted by outliers, missing data, and measurement errors. Which term describes this data sensitivity?

Data sensitivity describes how regression estimates react to imperfect data. Linear regression relies on clean data, and outliers, missing values, or measurement errors can pull the estimated line away from the true relationship, changing both the slope and intercept. So this term captures how fragile the model’s results are to data quality. The slope and intercept are simply the estimated parameters of the line, not descriptors of how sensitive those estimates are to data issues. Dummy variables, meanwhile, are just a way to encode categorical predictors and don’t address sensitivity to data quality. If you’re concerned about data sensitivity, you’d look at robustness techniques and diagnostic measures that show how influential problematic observations are.

Data sensitivity describes how regression estimates react to imperfect data. Linear regression relies on clean data, and outliers, missing values, or measurement errors can pull the estimated line away from the true relationship, changing both the slope and intercept. So this term captures how fragile the model’s results are to data quality. The slope and intercept are simply the estimated parameters of the line, not descriptors of how sensitive those estimates are to data issues. Dummy variables, meanwhile, are just a way to encode categorical predictors and don’t address sensitivity to data quality. If you’re concerned about data sensitivity, you’d look at robustness techniques and diagnostic measures that show how influential problematic observations are.

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