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To solve this task, we need to develop a cross-tabulation of the given data. We are asked to divide the \(x\) values into classes of \(10-29\), \(30-49\), and so on, while dividing the \(y\) values into classes of \(40-59\), \(60-79\), and so on.

### Step-by-Step Process

1. **Identify Classes for \(x\):**
   - 10-29
   - 30-49
   - 50-69
   - 70-89

2. **Identify Classes for \(y\):**
   - 40-59
   - 60-79
   - 80-99

3. **Count Observations in Each Class Combination:**

| \(x\) \(\backslash\) \(y\) | 40-59 | 60-79 | 80-99 | Total |
|----------------------------|-------|-------|-------|-------|
| 10-29                      |   3   |   1   |   4   |   8   |
| 30-49                      |   1   |   3   |   1   |   5   |
| 50-69                      |   1   |   2   |   1   |   4   |
| 70-89                      |   0   |   1   |   2   |   3   |
| **Total**                  |   5   |   7   |   8   |  20   |

### Explanation:

- For \(x = 10-29\), count how many corresponding \(y\) values fall into each of the \(y\) ranges.
  - \(y = 72, 99, 58, 34, 60, 22, 77, 85, 45, 47, 98, 71, 32, 81, 64, 68, 67, 39, 28\)
- We repeat this process for the other \(x\)-classes and fill in the table.

Would you like more details on any of these steps or have questions? Here are five related questions to expand on this:

1. How do you calculate marginal totals in a cross-tabulation?
2. Can cross-tabulation help in identifying correlations between variables?
3. What is the importance of choosing appropriate class intervals in cross-tabulation?
4. How could you visualize this cross-tabulation using a heatmap?
5. What other data analysis techniques could be applied to this dataset?

**Tip:** Cross-tabulation is especially useful for categorical data analysis and identifying relationships between two variables at different levels.

Develop a cross-tabulation for the data, with x as the row variable and y as the column variable. For x, use classes of 10-29, 30-49, and so on; for y, use classes of 40-59, 60-79, and so on.

Learn how to create a cross-tabulation for the provided bivariate data, dividing X into classes (10-29, 30-49, etc.) and Y into classes (40-59, 60-79, etc.). This example highlights key concepts in bivariate data analysis and classification, suitable for high school students.

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