- #1
Whenry
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How do I handle the following situation.
I have two time series, A and B. For the entire 100 yrs, we have P(A|B) ≠P(A), and Chi squared test leads us to reject the hypothesis of independence.
But, If I break the data down into 10 yr chucks, I find that for some 10 yr chucks P(A|B) = P(A).
So it seems maybe there is a third variable C, and I should be looking at P(A|B,C).
Similarly,
if I use regression on some data and find a significan relationship, for 100 yrs, of
Y = b_0 + b_1*x_1 + ε
but when I break down into 10 yr chucks, I find some years where we have
Y = b_0 + ε.
So it seems I should be looking at some interaction term :
Y = b_0 + b_1*x_1*α_1
but I have no idea what α_1 is...
Any suggestions as to how to handle this? resources that help explain how to proceed?
thank you,
Will
I have two time series, A and B. For the entire 100 yrs, we have P(A|B) ≠P(A), and Chi squared test leads us to reject the hypothesis of independence.
But, If I break the data down into 10 yr chucks, I find that for some 10 yr chucks P(A|B) = P(A).
So it seems maybe there is a third variable C, and I should be looking at P(A|B,C).
Similarly,
if I use regression on some data and find a significan relationship, for 100 yrs, of
Y = b_0 + b_1*x_1 + ε
but when I break down into 10 yr chucks, I find some years where we have
Y = b_0 + ε.
So it seems I should be looking at some interaction term :
Y = b_0 + b_1*x_1*α_1
but I have no idea what α_1 is...
Any suggestions as to how to handle this? resources that help explain how to proceed?
thank you,
Will