Advantage and disadvantage of regression flexibility How would I answer this question? Describe one advantage and one disadvantage of flexible vs less flexible approaches for regression. Under what conditions might a less flexible approach be preferred? I am not entirely sure how to answer this question. By 'flexible' I think this is related to how well the function fits the data points? So maybe a flexible approach may be including splices since they model the data points very accurately? Which therefore would make a less flexible one just the basic linear model since this is just straight so would be very rigid and not predict the model as well as a non-linear model/splice? I'm not too clear on this so any help would be much appreciated thank you!

One advantage of a flexible approach for regression is that a more flexible model allows us to take full advantage of a large sample size (n). One disadvantage of a flexible approach for regression is that a less flexible approach would be preferred if the sample size (n) is very small. Although both models (whether more flexible or less flexible) would not yield a good enough prediction, the more flexible model would tend to overfit the data and would perform more poorly than the less flexible one.