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A politician selects a random sample of 306 voters from her state. For each voter, she records the region in which they live and their political affiliation. She would like to know if there is convincing evidence that the region in which voters live is associated with their political affiliation. Let \(\alpha = 0.05\). The observed counts are displayed in the table.

Are the conditions for inference met?

A. No, the data do not come from a random sample.
B. No, the 10% condition is not met.
C. No, the Large Counts condition is not met since all expected counts are not greater than 5.
D. All conditions for inference are met.

Answer :

Based on the hypothesis, the correct option is C. No, the Large Counts condition is not met since all expected counts are not greater than 5.

How to explain the information

The statement mentions that the politician selects a random sample of 306 voters from her state. However, it also states that the data do not come from a random sample. This contradiction suggests that the random sample condition is not met.

The Large Counts condition for inference states that all expected counts must be greater than 5. In this case, the expected counts are:

Region 1: 103 * 0.4 = 41.2

Region 2: 103 * 0.3 = 30.9

Region 3: 103 * 0.3 = 31.9

As you can see, not all of these expected counts are greater than 5. Therefore, the Large Counts condition is not met and we cannot use the chi-square test for independence to test the politician's hypothesis.

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