Regressions allow you to validate a correlation while allowing to identify the equation that connects the data and thus predict behaviors.
Simple linear regressions are used to identify a correlation between 2 variables and to quantify this relationship.
In many cases we have clear correlations and yet we do not know how to quantify them because they are nonlinear.
A phenomenon rarely has a single root cause. Often we are faced with a multitude of parameters having a more or less strong influence on the result. That is the whole issue of multiple regressions.
Logistic regression is a mathematical model for defining a regression model when the variable to be explained is qualitative.