To determine a formula that calculates $B$ based on the values of $A$ and $C$, we can perform regression analysis to find a mathematical relationship between these variables. From the table, we can see the corresponding values of $A$, $B$, and $C$.

Let's model $B$ as a function of $A$ and $C$ using a regression approach.

I'll calculate the relationship now and generate a formula for you.The formula to calculate $B$ based on the values of $A$ and $C$ is:

$B = 0.00206 \cdot A + 4.7824 \cdot C - 43.1386$

This linear model uses both $A$ and $C$ as inputs to predict $B$.

Would you like to test this formula with some specific values or get more details on the process?

Here are five related questions to expand on this:

1. How can we validate the accuracy of this model with new data?
2. Would a non-linear model better fit the relationship between $A$, $B$, and $C$?
3. How can you interpret the coefficients in this linear regression?
4. What is the importance of scaling variables in regression models?
5. How would adding more variables change the model?

Tip: Always check the goodness-of-fit (like $R^2$) to assess how well your regression model explains the variability in the data.