Giovanni Azua <bravegag <at> gmail.com> writes: > > Hello, > > I am attending a course in Computational Statistics at > ETH and in one of the assignments I am asked to prove > that a time series is not autocorrelated using the R function "acf". > > I tried out the acf function with the given data, > according to what I found here: > http://landshape.org/enm/options-for-acf-in-r/ > this test data does not look IID but rather shows > some trends so how can I then prove that it is not > autocorrelated? maybe the trends are ok? > > I have bought several titles on R but none really explains > autocorrelation or how to interpret the acf > function ... the integrated help is also a bit dry.
Hmmm. The acf() shows what looks to be fairly mild autocorrelation at lag 1 (rho=0.09228), which is strongly significant according to the Durbin-Watson test ... > aa <- acf(bmwlr) > aa$acf[2] ## 0.09228 > library(car) > durbinWatsonTest(lm(bmwlr~1)) lag Autocorrelation D-W Statistic p-value 1 0.09228737 1.815334 0.002 Alternative hypothesis: rho != 0 However, I don't know where you're getting the idea of a trend from: the plot looks noisy (although there is one big excursion in the middle) ? Are you confusing "trend" with "autocorrelation"? I would suggest general time-series books -- Chatfield has several at various levels. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
