Final answer:
When NAs are introduced by coercion, they can be treated by replacing them with a specific value or using advanced techniques like multiple imputation. The treatment depends on the context and the analysis being performed.
Step-by-step explanation:
When NAs (missing values) are introduced by coercion, they can be treated in different ways depending on the context. One common approach is to replace the NAs with a specific value, such as the mean or median of the existing data. This is often done in statistical analysis where missing values can affect the accuracy of the results. Another approach is to use advanced techniques like multiple imputation, which involves creating multiple plausible values for each missing observation based on the existing data and statistical models.
For example, let's say we have a dataset of students' test scores and one of the variables is age. If some of the age values have been coerced to missing (NA), we can decide how to treat those missing values based on the analysis we want to perform. If we're interested in the average age of the students, we could calculate the mean age of the non-missing values and replace the NAs with that value. On the other hand, if we're interested in the relationship between age and test scores, we might use multiple imputation to generate plausible age values for the missing observations and incorporate them into our analysis.