The regression equation given in the problem is:

$\hat{y} = -0.0127 + 0.0280x$

where $x$ is the number of beers consumed, and $\hat{y}$ represents the predicted blood alcohol content (BAC).

We are asked to predict Sam's BAC if he consumes 5 beers and determine how much above the legal limit of 0.08 this would be.

1. Substitute $x = 5$ into the equation to find $\hat{y}$: $\hat{y} = -0.0127 + 0.0280 \times 5$

2. Calculate the value of $\hat{y}$: $\hat{y} = -0.0127 + 0.140 = 0.1273$

3. Now, compare this value with the legal limit: $0.1273 - 0.08 = 0.0473$

Thus, according to the model, Sam's BAC would be 0.0473 above the legal limit if he consumes 5 beers.

Would you like further details or have any questions?

Here are some related questions to expand on this topic:

1. How does the slope of the regression equation affect predictions?
2. What would Sam's BAC be if he consumed a different number of beers, such as 3 or 7?
3. How does the intercept in this regression equation influence the predictions?
4. What are potential limitations of using this model to predict BAC accurately?
5. How could the accuracy of this model be tested or validated?

Tip: When interpreting regression models, always check if the relationship between variables makes practical sense and if assumptions are met for accuracy in predictions.