Power regression finds a relationship in the form $y = ax^b$, where $a$ and $b$ are constants to be determined.

To solve this, I will:

1. Linearize the data by taking the logarithms of both $x$ and $y$.
2. Apply linear regression on the transformed data.
3. Convert back to the power form.

Let's go through the steps. I will calculate this equation for you.The power function that best fits the given data is:

$y = 105.334 \cdot x^{1.511}$

Would you like more details on how this was calculated or have any other questions?

Here are five related questions for further exploration:

1. How would the power function change if we extended the data range?
2. Can power regression be applied to any type of data, or are there specific conditions?
3. How does power regression compare to exponential or polynomial regression?
4. What are the real-world applications of power functions in modeling data?
5. How does the correlation coefficient affect the interpretation of the power model?

Tip: When interpreting regression models, always check the residuals to ensure the model fits the data well.