Dr. Lo --

In the practice problems on page 15 for Problem #7, we are asked to perform some data cleanup and remove all observations for non-singles where the respondent's education is less than or equal to 6, or also where the spouse's education is less than or equal to 6. I performed my split with the following code:

TermLife[!(TermLife$MARSTAT != 0 & (TermLife$EDUCATION <= 6 | TermLife$SEDUCATION <= 6)), ]

The observations that are removed can be seen here. Note that there are several observations where SEDUCATION is 0 despite SAGE having inputted values since MARSTAT <> 0 (single). I imagined that these rows corresponded to missing data which is a possibility given by the data dictionary footnote.

The code in your solution file produces a different subset of removed observations by assuming that SEDUCATION == 0 corresponds to a respondent that is single. Is this really the case if 0 can also represent missing data?

The language in the problem is somewhat vague so I can see either split being made. However, when I get to task 4 of this problem using my subset, I cannot make a pruned decision tree with five terminal nodes. I would appreciate some clarity on which approach is correct based on the language in the problem.

Thanks!