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. These are the books I have on R: - Introductory Statistics with R (Springer) - A handbook of Statistical Analyses using R (CRC) - R in a Nutshell (Oreilly) - Statistical Computing with R (CRC) Thanks in advance, Best regards, Giovanni # ========================================================================================= # Computational Statistics # Series 4 # Author: Giovanni Azua # Date: 16 April 2010 # ========================================================================================= rm(list=ls()) # clear workspace # ========================================================================================= # EXERCISE 1.(c) # ========================================================================================= # load dataset from web #bmwlr <- scan("http://stat.ethz.ch/Teaching/Datasets/bmw.dat";) # load dataset from file bmwlr <- scan("/Users/bravegag/code/compstats/bmw.dat") par(mfrow=c(1,2)) # visualize two plots acf(bmwlr, lag.max = 10) acf(bmwlr^2, lag.max = 10) [[alternative HTML version deleted]] ______________________________________________ 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.
