I was experimenting with
cabbages data set and linear regression in
R. I tried a Durbin-Watson test on model "Vitamin C concentration as function of cabbage head weight" and got significant result of autocorrelation:
data(cabbages, package = "MASS") lmtest::dwtest(VitC ~ HeadWt, alternative="two.sided", data=cabbages)
Durbin-Watson test data: VitC ~ HeadWt DW = 1.2929, p-value = 0.003546 alternative hypothesis: true autocorrelation is not 0
1) How should I interpret this result of significant autocorrelation in this context?
2) Does it mean that linear regression is not suitable for this data set? If yes, what are the alternatives?
3) Is Durbin-Watson test appropriate in this case, as it is not time-series?
I read several post on Durbin-Watson test (e.g., 1, 2, 3). I noticed, that usually it is mentioned in context of econometrics ant time series analysis but do not clearly understand in what situations it is appropriate to use this test and in what situations it is not.