The linear regression model does not perfectly fit the data, as indicated by the non-random pattern and varying spread of residuals. There may be some underlying structure in the data that a linear model cannot fully capture, suggesting that a different model (perhaps non-linear) might better represent the relationship between the variables. rephrase like a 8th grader

Answer 1

The linear regression model doesn't fit the data perfectly. This is shown by the way the leftover differences (called residuals) show a pattern and aren't spread out evenly. This means there might be something going on in the data that a straight line can't really explain. We might need to use a different type of model, maybe a non-linear one, to better understand the relationship between the variables.