Yes we can, in case when we know that there is a relationship between the studied factor and the studied outcome and its mechanism is known.

The correlation analyzes aimed to find the relation between the (any) variables, whereas the regression aimed to how much is this relation, and what is its curve, when one variable is the cause of change of the second variable, no matter how strong is this change.

When we can (or we beleive we can) explain the correlation found, we can start with the hypothesis that one event is the cause of another, then suggest the model how this relationship delivers (that can be realized using the regression analyses).

It's not make a sense to use regression analyses when you found, for example, the correlation between the blood proteins "A" and "B" if you dont have an appropriate explanation why they are in relation.

This book attached could also be helpful.

If you have enough observations, such as 40 or 50 or more, then tested your data for correlation, it is recommended to run regression, for it will give you better underdtanding of your data. You can check any reference of Statistics books to understand better. Regards.

You could look at chapters IV and XIV in the Online Stats Book:

• https://onlinestatbook.com/2/index.html

Another nice but somewhat more advanced resource for learning about regression is the 501 course here:

• https://online.stat.psu.edu/statprogram/courses

Unfortunately, there are some issues with the 501 course site this morning that make me wonder if it has been hacked. I have alerted the folks at PSU, and with any luck, they'll get it fixed soon.

UPDATE: The STAT 501 website is working normally again. ;-)