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The problem asks for a hypothesis test to determine if the percentage of smokers has reduced after the implementation of smoking cessation programs, based on the previous estimate of 35%.

In hypothesis testing, we define the null hypothesis \(H_0\) and the alternative hypothesis \(H_a\).

1. **Null Hypothesis (\(H_0\)):** This is the hypothesis that there is no change or effect. In this case, the null hypothesis is that the percentage of smokers is still 35%, so:
   \[
   H_0: p = 0.35
   \]
   
2. **Alternative Hypothesis (\(H_a\)):** This is the hypothesis that reflects the research question. Since you believe that the percentage has reduced, the alternative hypothesis should be:
   \[
   H_a: p < 0.35
   \]

So, for the drop-down options:

- For \(H_0\), select \(=\) and 0.35.
- For \(H_a\), select \(<\) and 0.35.

Do you need further details on the steps involved in hypothesis testing or have any questions?

Here are some related questions:
1. What is the next step in conducting this hypothesis test after setting the hypotheses?
2. How do we determine the significance level (\(\alpha\)) for the test?
3. What kind of data do we need to perform this test (e.g., sample size, proportion)?
4. What is a one-tailed test, and why is it relevant here?
5. How do we calculate the test statistic for this hypothesis test?

**Tip:** Always clearly define your hypotheses before performing any hypothesis test, as it sets the stage for the entire analysis.

Past studies have indicated that the percentage of smokers is estimated to be about 35%. Given the new smoking cessation programs, you now believe that the percentage of smokers has reduced. What are the null and alternative hypotheses?

Learn how to formulate null and alternative hypotheses for a hypothesis test determining if the proportion of smokers has reduced after cessation programs. This problem explores hypothesis testing for proportions using statistical concepts. Suitable for college-level statistics students.

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