The Lower Canyon stretch had some extreme data values. Compute a 5% trimmed mean for this region, and compare this result to the mean for the Upper Canyon computed in part (a). (Enter your answer to 2 decimal places.)

To calculate the 5% trimmed mean for the Lower Canyon stretch, follow these steps:

1. **Arrange the data in ascending order**: This is required to identify the extreme values.

2. **Trim 5% from both ends**: Since you want a 5% trimmed mean, you need to remove the lowest 5% and the highest 5% of the data. To do this, multiply the number of data points by 5%. For example, if you have 100 data points, remove 5 data points from each end.

3. **Compute the mean of the remaining data**: After trimming, compute the average of the remaining data values.

4. **Compare with the Upper Canyon mean**: After you compute the trimmed mean for the Lower Canyon, compare it with the Upper Canyon mean that you already calculated in part (a).

To proceed, please provide the dataset for the Lower Canyon stretch or the number of observations so that I can calculate the 5% trimmed mean.

Would you like help with uploading the data, or do you want to enter it directly?

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Here are 5 follow-up questions for further exploration:

1. How is the 5% trimmed mean affected by outliers?
2. What is the advantage of using a trimmed mean over the regular mean?
3. How does the 5% trimmed mean compare to other types of means (e.g., median or mode)?
4. Could you calculate a different trimmed mean, such as 10% or 15%? How does it change the result?
5. What are some real-world scenarios where a trimmed mean is more useful than a simple mean?

**Tip:** Trimming a small percentage of extreme values helps reduce the influence of outliers, giving a better representation of the central tendency in skewed datasets.

Learn how to calculate a 5% trimmed mean for the Lower Canyon stretch by trimming extreme data values, and compare this result with the Upper Canyon mean computed in a previous step. This guide covers the step-by-step process of trimming 5% from both ends of the data and computing the central tendency for skewed datasets. Ideal for college-level statistics students.

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