Question

the weight, y, in pounds, of human babies was tracked for the first 12 weeks after birth, where t represents the number of weeks after birth. the linear model representing this relationship is y = 8.7 + 0.52t. douglas wanted to predict the weight of a baby at 16 weeks.
what is this an example of, and is this method a best practice for prediction? explain your reasoning.
this is an example of extrapolation. extrapolation is not a best practice for prediction, as the prediction is not accurate because 16 weeks is outside the given interval of 12 weeks.
this is an example of extrapolation. extrapolation is a best practice for prediction, as the prediction is accurate even though 16 weeks is outside the given interval of 12 weeks.
this is an example of linear modeling. linear modeling is a best practice for prediction, as the prediction is accurate even though 16 weeks is outside the given interval of 12 weeks.
this is an example of linear modeling. linear modeling is not a best practice for prediction, as the prediction is not accurate because 16 weeks is outside the given interval of 12 weeks.

Explanation:

Predicting a baby's weight at 16 weeks using a model built from data only up to 12 weeks is extrapolation, as 16 weeks falls outside the range of the original data used to create the linear model. Extrapolation is not a best practice for this prediction because the linear trend observed in the first 12 weeks may not continue beyond that interval, making the prediction unreliable.

Answer:

This is an example of extrapolation. Extrapolation is not a best practice for prediction, as the prediction is not accurate because 16 weeks is outside the given interval of 12 weeks.