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Ok, that makes sense! Thank you very much! :-)

Ok now I think I got it!
We therefore cannot reject the null hypothesis that this is white noise (therefore it seems like it indeed is white noise), right?
thank you very much for your effort!

Oh sorry. Yes of course. But if one gets this result with a regular AR(1) model, since one cannot test it with the dicky fuller test. What I mean is: if I have some data (e.g. financial time series), and I want to know more about the phi, then I use a AR(1) model. And then I have a coefficient for p...

So that means that if we have a coefficient SERIES2(-1), a value 0.0028 but a p-value of 0.89, that means that we do indeed have to reject the null hypothesis that this is white noise? even though the coefficient phi is that low?
Thank you!

Perfect, thank you!

Hi I am a little bit confused. I want to interpret an AR(1) Model for a financial time series and find out, whether Phi, so coefficient is equal to one (random walk), between one and zero (stationary) or equal to zero (white noise) is. not when I have a phi, but the p-value is above the significance...