You have reason to believe that there are actually more raisins in each box than the company claims. Your alternative hypothesis, \( H_{a} \), would be \( \mu<200 \) \( \mu>200 \) \( \mu=200 \) \( \mu \neq 200 \) DONE

In this scenario, if you suspect that there are more raisins than the company claims, your alternative hypothesis \( H_a \) should reflect that expectation. Therefore, \( \mu > 200 \) would be appropriate, as it suggests that the true mean number of raisins per box exceeds the stated amount. When testing hypotheses, it's also crucial to establish your null hypothesis, which for this case would be \( H_0: \mu = 200 \). This helps gauge whether the observed data can provide sufficient evidence to reject the null in favor of the alternative. Remember, clearly defining your hypotheses sets the stage for a rigorous statistical test!