There are plenty of procedures for checking the validity of those assumptions.
For independence of observations (3 in you numbering), you usually plan for it during the experiment's design phase, or handle it through different techniques.
Numbers 4 and 6 can be done by:
- analyzing the residuals plots against the dependent variable and histogram. If it looks uniformly random and the histogram looks like a Gaussian variable, you are usually good to go. But some people prefer a more "rigorous" approach through tests, so I recommend checking this page and this page. There are many different approaches each with its strengths and weakness. Also, there are different types of residuals (transformations of the residuals, or generated with different procedures) please refer to this blog post for an overview of a few of them.
For outliers, it is usually important to take care of them since they can bias your estimates. Again there are many different ways to detect and account for this problem. Cook's distance is one of them. Usually robust regression methods are used to account for outliers without detecting and removing them.