Which term describes extreme observations that can bias regression results?

Which term describes extreme observations that can bias regression results?

• A. Ridge
• B. Residual Plots
• C. Outliers
• D. Target Variable

Answer: (C)

Outliers are extreme observations that can bias regression results. When a data point lies far from the overall pattern, it can pull the regression line toward itself, changing the estimated slope and intercept and, in turn, altering predictions and model metrics for the rest of the data. This is why detecting and handling outliers is important in regression analysis: they can distort the fit and lead to misleading conclusions about relationships between variables. Ridge is a regularization technique that shrinks coefficients to avoid overfitting, not specifically defined by extreme observations. Residual plots are diagnostic tools used to assess how well the model fits the data by examining residual patterns, not to describe extreme observations themselves. The target variable is simply what you’re trying to predict, not a description of extreme data points.

Outliers are extreme observations that can bias regression results. When a data point lies far from the overall pattern, it can pull the regression line toward itself, changing the estimated slope and intercept and, in turn, altering predictions and model metrics for the rest of the data. This is why detecting and handling outliers is important in regression analysis: they can distort the fit and lead to misleading conclusions about relationships between variables.

Ridge is a regularization technique that shrinks coefficients to avoid overfitting, not specifically defined by extreme observations. Residual plots are diagnostic tools used to assess how well the model fits the data by examining residual patterns, not to describe extreme observations themselves. The target variable is simply what you’re trying to predict, not a description of extreme data points.