The correct answer is: C. Outliers cannot conform to the regression relationship.
Outliers are data points that are significantly different from the rest of the data. They can be the result of spurious or real processes. Outliers can have varying degrees of influence on the regression line. If an outlier is far enough away from the rest of the data, it can pull the regression line in its direction. This can make it difficult to interpret the regression line.
Outliers can be removed from the data set, but this can sometimes lead to a loss of information. It is often better to keep the outliers in the data set and to use a robust regression method. Robust regression methods are designed to be less affected by outliers than traditional regression methods.
Here is a brief explanation of each option:
- A. Outliers can have varying degrees of influence. This is true. Outliers can have a small or large influence on the regression line, depending on how far they are from the rest of the data.
- B. Outliers can be the result of spurious or real processes. This is also true. Outliers can be the result of random errors or they can be the result of real processes that are not captured by the model.
- D. None of the mentioned. This is not true. Option C is incorrect.
I hope this helps! Let me know if you have any other questions.