Final answer:
For inference statistics regarding the new public parking garage fees, the correct assumptions to make are that the data represent a random sample of all days and that data values should be independent. The Central Limit Theorem ensures that the sample mean distribution approximates normality given the sufficient sample size in this scenario. Therefore, the correct option is b.
Step-by-step explanation:
To draw valid inferences from the average daily fees collected from a new public parking garage, we need to ensure certain statistical assumptions are met. When it comes to inference statistics, here are the assumptions that usually need to be considered:
• The data should represent a random sample of the population.
• The sample size should generally be less than 10% of the population to avoid sampling without replacement issues unless one assumes a very large population.
• The distribution of the sample means should be approximately normal. This can be assured through the Central Limit Theorem if the sample size is large enough, typically n ≥ 30, which is the case here, or if the population distribution is normal.
• Data values should be independent of each other.
Based on these points, the correct assumptions that must be met for inference are:
The data are a random sample of all days (option b) since it's necessary for the sample to represent the population adequately. Next, while the distribution of individual days' revenue does not necessarily need to be unimodal and symmetric, we can rely on the Central Limit Theorem (CLT) for the distribution of the sample mean, which states that the distribution of the sample mean will be approximately normal if the sample size is large enough. Lastly, the assumption that the data values should be independent is also necessary (option d), which means the collection of parking fees on one day does not influence the collection on another.