We could do this for all of the LBMs of interest. The regression line does two important things. These benefits don't come for free. The method is valid only insofar as the data follow a straight line, which is why it is essential to examine scatterplots. Interpolation and Extrapolation Interpolation is making a prediction within the range of values of the predictor in the sample used to generate the model.
Interpolation is generally safe.
One could imagine odd situations where an investigator collected responses at only two values of the predictor. Then, interpolation might be uncertain since there would be no way to demonstrate the linearity of the relationship between the two variables, but such situations are rarely encountered in practice. Extrapolation is making a prediction outside the range of values of the predictor in the sample used to generate the model. The more removed the prediction is from the range of values used to fit the model, the riskier the prediction becomes because there is no way to check that the relationship continues to be linear. For example, an individual with 9 kg of lean body mass would be expected to have a strength of -4.9 units.