The correct answer is: C. A or B depend on the situation
Residuals are the difference between the observed values and the fitted values in a regression model. In general, lower residuals are better, as they indicate that the model is a better fit to the data. However, there are some cases where higher residuals may be preferable. For example, if you are trying to predict a continuous variable, you may want to have some variation in the residuals so that you can identify outliers. Additionally, if you are trying to predict a binary variable, you may want to have some variation in the residuals so that you can identify cases that are difficult to predict.
Here is a more detailed explanation of each option:
- Option A: Lower is better. This is the most common case, as lower residuals indicate that the model is a better fit to the data. For example, if you are trying to predict the price of a house, you would want the residuals to be as close to zero as possible. This would indicate that the model is able to accurately predict the price of houses.
- Option B: Higher is better. This is less common, but there are some cases where higher residuals may be preferable. For example, if you are trying to predict a continuous variable, you may want to have some variation in the residuals so that you can identify outliers. Outliers are data points that are very different from the rest of the data. They can be caused by errors in the data or by unusual circumstances. By identifying outliers, you can investigate them further to see if they are valid data points or if they should be removed from the analysis.
- Option C: A or B depend on the situation. This is the most accurate option, as it recognizes that there is no single answer to the question of whether lower or higher residuals are better. The best answer depends on the specific situation and the goals of the analysis.
- Option D: None of these. This option is incorrect, as it does not recognize that there are some cases where higher residuals may be preferable.