To find the best-fitting exponential equation of the form $y = ab^x$ for the given data, we'll follow the same steps as before:

1. Transform the data: Convert the $y$ values to their logarithms so the equation becomes linear.
2. Apply linear regression: Perform linear regression on $\log(y)$ versus $x$ to find the slope and intercept.
3. Determine $a$ and $b$: Calculate the values of $a$ and $b$ from the slope and intercept.

Let me calculate this now.The exponential equation that best fits the new data is:

$y = 734.03 \times (1.584)^x$

Where $a \approx 734.03$ and $b \approx 1.584$.

Would you like any further details or have any questions about this calculation? Here are some related questions you might find useful:

1. How does changing the dataset affect the values of $a$ and $b$ in the exponential equation?
2. What is the significance of the base $b$ in an exponential growth model?
3. How can you interpret the slope of the linearized regression in terms of the original exponential model?
4. What are some real-world examples where exponential regression might be useful?
5. How can you determine the goodness of fit for this exponential model?

Tip: When comparing different exponential models, the value of $b$ gives you a quick sense of how rapidly the dependent variable is increasing or decreasing.