https://file.aiquickdraw.com/mathbot/file/1727185129893egp52t5i.jpg

By looking at the scatter plot in the image, we can see that the data points follow a downward curving trend that flattens out as \( x \) increases. This behavior is characteristic of an **exponential decay** pattern, where the data decreases rapidly at first and then levels off.

Hence, the best-fitting regression model for this data would be:

**D. Exponential**

Would you like to know more details about regression models or ask any questions?

Here are 5 related questions you might find useful:
1. What is the difference between exponential and linear regression models?
2. How do you determine the equation of an exponential regression model?
3. What are some real-world examples where exponential decay is observed?
4. How is a regression model different from a curve fitting technique?
5. Can we use polynomial regression to fit this data? If so, how would it differ?

**Tip:** When identifying regression types visually, always look for the trend of the data over both small and large values of \( x \).

By visual inspection, determine the best-fitting regression model for the data plot below.

Learn how to visually identify the best-fitting regression model for a data plot. This problem focuses on the exponential decay pattern, where data points decrease rapidly and then level off. Suitable for students in Grades 9-12.

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