I understand this is a broad question, but I have been looking all over and can't seem to find a straight answer.
Linear regression is based on a few assumptions, and you should always check to see if they are satisfied, but what if they aren't? Does that automatically mean that any results are invalid? Furthermore, some assumptions aren't necessarily yes or no questions. For example multicollinearity - it seems like it is okay if there is a little bit, but how much is too much? Especially in a business setting, what would you do if an assumption is not true or cannot be verified?