I’d like to test whether my time series of consecutive payments is independent or not and thought that since this is a pretty common condition in statistics it should be easy. Well, turns out is isn’t – at least for me.

Now I found these earlier posts which have driven me into the Ljung-Box or Box-Pierce direction:

Testing normality and independence of time series residuals

What is a reasonable independence test for a time series?

I'm working with R and at first I wanted to check the test and if I correctly understand its p-value. So I thought of the following vector and expected a quite low p-value but now I’m surprised about the high number (0.5391) and its implication on not to reject the hypothesis of “independently distributed”.

library(stats)
test<-c(1,1,1.1,1,1,1,1,1,1.1)
Box.test(test,lag=1,type = "Ljung-Box")

So what am I getting wrong or is there a different/better way to test the independence of my time series where I don't know its distribution?

I’d like to test whether my time series of consecutive payments is independent or not and thought that since this is a pretty common condition in statistics it should be easy. Well, turns out is isn’t – at least for me.

Now I found these earlier posts which have driven me into the Ljung-Box or Box-Pierce direction:

Testing normality and independence of time series residuals

What is a reasonable independence test for a time series?

I'm working with R and at first I wanted to check the test and if I correctly understand its p-value. So I thought of the following vector and expected a quite low p-value but now I’m surprised about the high number (0.5391) and its implication on not to reject the hypothesis of “independently distributed”.

library(stats)
test<-c(1,1,1.1,1,1,1,1,1,1.1)
Box.test(test,lag=1,type = "Ljung-Box")

So what am I getting wrong or is there a different/better way to test the independence of my time series where I don't know its distribution?