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CompTIA DataX DY0-001 (V1) Practice Question

A customer analytics team is cleaning a dataset that contains customer age (fully observed), loyalty tier (fully observed), and total annual spending, of which about 18 % of the values are missing. Exploratory analysis shows that customers who are younger and those in the highest loyalty tier are less likely to report spending. However, within any given age-tier combination, the probability that spending is missing is unrelated to the true (unobserved) spending amount. Which description best characterizes the missingness mechanism for the spending variable in this situation?

  • Missing Completely at Random (MCAR); missingness is unrelated to any observed or unobserved variables.

  • Missing Completely at Random due to a random data-entry glitch that uniformly deleted 18 % of spending values across the dataset.

  • Missing Not at Random (MNAR); higher or lower spending directly influences the chance that the value is missing, even after accounting for age and tier.

  • Missing at Random (MAR); the probability of a missing spending value depends only on the observed age and loyalty tier.

CompTIA DataX DY0-001 (V1)
Mathematics and Statistics
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