The regression equation is:

$\hat{y} = -165 + 41.1x$

Where:

• $\hat{y}$ is the predicted weight,
• $x$ is the overhead width in cm.

Using this equation, the predicted weight of a seal with an overhead width of 1.6 cm is approximately:

$\hat{y} = -165 + 41.1(1.6) = -98.83 \, \text{kg}$

This prediction is clearly unrealistic because weight cannot be negative. The issue arises because the overhead width of 1.6 cm is far outside the range of the data provided (which ranges from 7.1 to 9.8 cm). Therefore, the prediction is invalid since it's an extrapolation beyond the meaningful range of the data.

Would you like more details, or do you have any questions? Here are five related questions you might find interesting:

1. How does extrapolation affect the accuracy of predictions?
2. Can we improve this model by adding more data points within a realistic range?
3. What does the $r$-value indicate about the strength of this regression model?
4. What assumptions underlie the use of linear regression for this data?
5. How can we verify if linear regression is the best fit for this type of data?

Tip: Always check that the prediction inputs fall within the range of the original data to avoid inaccurate extrapolations.