To find an exponential equation of the form $y = ab^x$ that best fits the given data using regression, we'll follow these steps:

1. Take the logarithm of both sides:
   $\log(y) = \log(a) + x\log(b)$ This equation is now in the form of a linear equation:
   $\log(y) = \log(a) + \log(b) \cdot x$

2. Apply linear regression to the transformed data:
   We'll treat $\log(y)$ as the dependent variable and $x$ as the independent variable. The linear regression will give us values for $\log(a)$ and $\log(b)$.

3. Calculate $a$ and $b$:
   From the regression output, we can find: $a = 10^{\text{intercept}}$ $b = 10^{\text{slope}}$

Let's compute this now.The exponential equation that best fits the given data is:

$y = 672.47 \times (1.634)^x$

Where $a \approx 672.47$ and $b \approx 1.634$.

Would you like any further details or have any questions about the process? Here are some related questions you might find interesting:

1. How does the correlation coefficient indicate the strength of the fit for this regression?
2. Can the regression method be used for different types of functions besides exponential?
3. How does the linearization of an exponential function affect the regression analysis?
4. What are the implications of a higher or lower value of $b$ in an exponential model?
5. How can you assess the quality of the model fit using residuals?

Tip: In regression analysis, always check the residuals (the difference between observed and predicted values) to assess the goodness of fit of your model.