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From the image, you appear to be working on constructing a confidence interval and interpreting results. Here's a breakdown of the question:

1. **Procedure Name**: Based on the problem's requirements, it seems you've correctly identified the "1-Proportion Z-Interval." This method is used when estimating population proportions with a large sample size.

2. **Interval and Point Estimate**:
   - The point estimate (\(\hat{p}\)) is calculated as \(\frac{463}{3350}\).
     \[
     \hat{p} = \frac{463}{3350} \approx 0.1382
     \]
   - The confidence interval for \(p\) is given as \(0.126\) to \(0.151\).

3. **Conclusion**:
   - Confidence intervals are interpreted as: "We are [confidence level, typically 95%] confident that the true population proportion lies between the lower and upper bounds of the interval."

If you'd like help recalculating values or additional details, let me know!

### Related Questions
1. How is the standard error for a proportion calculated?
2. What factors determine the width of a confidence interval?
3. How does sample size impact the reliability of a confidence interval?
4. Why do we use a Z-interval instead of a T-interval for proportions?
5. How would increasing the confidence level affect the interval?

### Tip
Confidence intervals always provide a range for the parameter (e.g., a population proportion) based on sample data. Their accuracy depends on the sample size and confidence level.

The user appears to be working on constructing a confidence interval for a population proportion and interpreting the results.

This problem involves constructing a confidence interval for a population proportion using the 1-Proportion Z-Interval method. The point estimate is calculated as 463/3350, and the interval ranges from 0.126 to 0.151. Learn how to interpret and calculate confidence intervals step-by-step.

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