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.