Import “Data Cleaning Session_Main Data.csv”. Drop the KEY variable. Drop the pilot surveys and the pilot survey variable. Drop the entries that are perfect duplicates of other entries. For the remaining duplicate teacher IDs, recall how in the lesson we kept the observations that had more information and dropped the duplicates that had missing data in some fields. Now suppose that the entries with missing data are the more up-to-date entries that should be retained (e.g. perhaps the teachers contacted the enumerators later and requested their info to be erased). Keep these entries and drop the duplicated entries.
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1. How many remaining entries are missing values for class_scan_1?
2. What is the median classroom size for the entries with non-missing values for class_scan_1? Round to the nearest whole number.
3. Why is it inadvisable to use the ‘force’ option with the ‘duplicates drop [varlist]’ command?
4. How many variable names in the Data Cleaning dataset will change if you run this command: rename *tea* *TEACHER*
5. What are different ways that you can see the numeric values that correspond to each category in a categorical variable with a value label? (select all that apply)
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