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Exercise

Looking for Predictable Missingness

If data are missing completely at random, then you shouldn't be able to predict when a variable is missing based on the rest of the data. Therefore, if you can predict missingness then the data are not missing completely at random. So, let's use the glm() function to fit a logistic regression, looking for missingness based on affordability in the mort variable you created earlier. If you don't find any structure in the missing data - i.e., the slope variables are not significant - it does not mean that you have proven the data are missing at random, but it is plausible.

Instructions
100 XP
  • Create a variable indicating if the "borrower_race" is missing (equal to 9) in the mortgage data.
  • Create a factor variable of the "affordability" column.
  • Regress affordability_factor on borrower_race_ind and call summary() on it.
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