The correct answer is: True.
Residuals are the differences between the observed values and the fitted values of a model. They can be used to investigate the best model fit by looking at their distribution and pattern. If the residuals are randomly distributed around zero, then the model is a good fit. However, if the residuals are not randomly distributed, then the model may not be a good fit.
For example, if the residuals are all positive, then the model is overfitting the data. This means that the model is too complex and is trying to fit the noise in the data. On the other hand, if the residuals are all negative, then the model is underfitting the data. This means that the model is too simple and is not capturing all of the variation in the data.
Residuals can also be used to identify outliers. Outliers are data points that are very different from the other data points. They can be caused by errors in the data or by unusual events. Outliers can affect the fit of a model, so it is important to identify them and investigate them further.
Overall, residuals are a valuable tool for investigating the best model fit. They can be used to identify problems with the model, such as overfitting or underfitting, and to identify outliers.