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To predict the number of clicks for 1,500 visits based on the data in the table, we can use a linear relationship between the number of visits and the number of clicks.

The data provided in the table includes pairs of website visits and the corresponding advertisement clicks. We will first determine the relationship between these two variables using a linear regression or a simple proportional method.

### Steps to estimate the relationship:
1. **Visits (X) to Clicks (Y)**:
   - We can calculate the click rate (Clicks/Visits) for each row.
   - Then, we can use the average click rate to predict the number of clicks for 1,500 visits.

Let's calculate the click rate for each row:

- For 153 visits: \( \frac{14}{153} \approx 0.0915 \)
- For 629 visits: \( \frac{38}{629} \approx 0.0604 \)
- For 471 visits: \( \frac{30}{471} \approx 0.0637 \)
- For 914 visits: \( \frac{53}{914} \approx 0.0580 \)
- For 307 visits: \( \frac{21}{307} \approx 0.0684 \)
- For 1,045 visits: \( \frac{60}{1,045} \approx 0.0574 \)
- For 510 visits: \( \frac{32}{510} \approx 0.0627 \)
- For 1,106 visits: \( \frac{63}{1,106} \approx 0.0570 \)

### Average click rate:
Now, we calculate the average of these click rates:
\[
\text{Average click rate} = \frac{0.0915 + 0.0604 + 0.0637 + 0.0580 + 0.0684 + 0.0574 + 0.0627 + 0.0570}{8} \approx 0.0651
\]

### Prediction for 1,500 visits:
Now we use the average click rate to predict the number of clicks for 1,500 visits:
\[
\text{Predicted clicks} = 1,500 \times 0.0651 \approx 97.65
\]
Rounding this result, we get approximately 98 clicks.

However, since 98 is not one of the options, the closest available answer is **D. 105**.

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Would you like more details on how this was calculated? Here are some related questions you could explore:
1. How would the result change if we used a regression model instead of an average click rate?
2. What if the number of visits was 2,000 instead of 1,500?
3. How does variability in the click rates impact our prediction?
4. What would happen if one of the click rates was an outlier? How would we adjust for it?
5. Can we create a graph to visualize the relationship between visits and clicks?

**Tip:** A simple proportional method works well when the data shows a roughly linear relationship. However, more complex models like regression could offer a more precise prediction.

Based on the table, what is the best prediction of the number of clicks on the advertisement if 1,500 people visit the website?

Learn how to predict the number of advertisement clicks based on a table of website visits and click data. This problem involves calculating average click rates and using proportional relationships, suitable for students in Grades 9-12.

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