Answer Happy

Which of the following statements is true about outliers when fitting a final model? If there are outliers in the data, they should be removed. Whatever final model is fit to the data would be very helpful if it ignores the most exceptional cases. o If there are outliers in the data, they should not be removed or ignored without a good reason. Whatever final model is fit to the data would not be very helpful if it ignores the most exceptional cases. Be cautious about using a categorical predictor when one of the levels has very few observations. When this happens, those few observations become influential points. The estimated intercept is the value of the response variable for the first category (i.e. the category corresponding to an indicator value of 0). The estimated slope is the average change in the response variable between the two categories. Points that fall horizontally away from the center of the cloud tend to pull harder on the line, so we call them points with high leverage. If there are outliers in the data, they should be standardized. Whatever final model is fit to the data will be better if the exceptional cases are standardized.