Final answer:
The variable most suitable for modeling with a normal distribution is option (c), sample means of a population, due to the Central Limit Theorem. This theorem states that with a large enough sample size, the distribution of sample means will be approximately normal regardless of the shape of the population distribution.
Step-by-step explanation:
When it comes to deciding which variables can be modeled using a normal distribution, we must understand that a normal distribution is appropriate for continuous data that tends to cluster around a central value. Thus, it isn't used for nominal data such as names or categories, like option (a). Option (b), the number of books MC students read in 2022, could potentially be normal if it's a large enough sample size and the data follows a bell-shaped pattern. Option (c), sample means of a population, especially for large samples, often follow a normal distribution due to the Central Limit Theorem. Lastly, option (d), the distribution of military ranks on an aircraft carrier, is categorical and does not follow a normal distribution; however, if we are discussing proportions of these ranks, they can be modeled by a normal distribution if certain conditions are met.
Given the typical uses of the normal distribution, such as in matched or paired samples and for assessing two population proportions, the best fit for a normal distribution scenario in the given question would be option (c).