The correct answer is: A. Linear regression is sensitive to outliers.
Outliers are data points that are very different from the rest of the data. They can have a significant impact on the results of a linear regression model. If an outlier is included in the model, it can cause the model to be biased. This means that the model will not be able to accurately predict the values of the dependent variable.
There are a few ways to deal with outliers in linear regression. One way is to remove them from the data set. However, this can be difficult to do, especially if the outlier is a legitimate data point. Another way to deal with outliers is to use a robust regression model. Robust regression models are designed to be less sensitive to outliers.
Here is a brief explanation of each option:
- Option A: Linear regression is sensitive to outliers. This means that outliers can have a significant impact on the results of a linear regression model. If an outlier is included in the model, it can cause the model to be biased. This means that the model will not be able to accurately predict the values of the dependent variable.
- Option B: Linear regression is not sensitive to outliers. This is not true. Linear regression is sensitive to outliers.
- Option C: Can’t say. This is not a correct answer. We can say that linear regression is sensitive to outliers.
- Option D: None of these. This is not a correct answer. Option A is the correct answer.