The answer to part **(c)**, the value of the test statistic, is:

\[
z \approx -2.718
\] 

This value was calculated using the formula:

\[
z = \frac{\hat{p} - p_0}{SE}
\]

Where:
- \(\hat{p} = 0.1345\) (sample proportion)
- \(p_0 = 0.20\) (proportion under the null hypothesis)
- \(SE \approx 0.0241\) (standard error)

The result is rounded to three decimal places as requested. 

Would you like further details or have any more questions?

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Here are 5 questions related to this problem:
1. What would be the test statistic if the sample proportion was higher than 20%?
2. How does the standard error impact the test statistic?
3. What happens if you round the intermediate values differently?
4. How would a two-tailed test change the process?
5. What is the critical value for a one-tailed test at a 0.05 significance level?

**Tip:** For hypothesis testing, it's important to maintain consistency in rounding during intermediate steps to avoid discrepancies in the final test statistic.

Explore the detailed calculation of the test statistic z ≈ -2.718 for hypothesis testing. This solution involves calculating z using sample proportion, null hypothesis proportion, and standard error. Learn about the impact of rounding on intermediate values and get answers to related questions.

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