Stochastic processes and regression analysis are just two sides of the same coin. Namely, Assume that you have a realization from a univariate time process and you postulate that the process that generated this data was autoregression of order , however with an unknown coefficient . I.e., , hence you can use statistics (regression analysis) in order to estimate . Now, assume that you are not sure what process generated your data and you are willing to test a set of AR(I)MA models. Here too the statistics might help you to select the most appropriate model. Namely, parametric-regression models allow you to approximate the data-generating process by a linear regression. Note that not every stochastic process can be well approximated by a linear-parametric regression model. And, basically, stochastic analysis assume that you know the properties of the process and you can work with them, while regression analysis assume that you don't know the data-generating process and you try to recover its properties by using the data.