What effect do outliers have in a regression model?

In a regression model, outliers can significantly distort estimates, which is why the correct answer is that they can influence the results in a major way. Outliers are data points that deviate markedly from other observations. When present, they can impact the slope of the regression line, skew coefficients, and ultimately lead to misleading interpretations of the relationship between independent and dependent variables.

For instance, if an outlier skews the results, the model may predict outcomes that do not accurately represent the underlying trend of the data. This can lead to inflated or deflated values for the regression coefficients, causing potential misinformed decisions based on the model's output. Analyzing a dataset without properly addressing outliers could result in a regression model that fails to generalize well to new data.

Other choices such as improving model accuracy or having no effect do not accurately represent the reality of how outliers interact with regression analysis. Additionally, saying that outliers are always removed during analysis ignores the nuances involved in data analysis; while they may sometimes be removed, they can also provide valuable insights or indicate specific variations within the data. Therefore, recognizing the substantial impact of outliers is crucial when building and interpreting regression models.