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)
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