Assignment grp.docx - Step:1 Initial steps of pulling in...

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Step:1. Initial steps of pulling in the file: > setwd("~/") > souvenirdata=read.table(file.choose(), header=TRUE, sep = "\t") > souvenirtimeseries <- ts(souvenirdata, frequency=12, start=c(1987,1)) Here by frequency =12 we mean that data will be shown monthly. > souvenirtimeseries Jan Feb Mar Apr May Jun Jul Aug 1987 2397.53 2840.71 3547.29 3752.96 3714.74 4349.61 3566.34 5021.82 1988 5198.24 7225.14 4806.03 5900.88 4951.34 6179.12 4752.15 5496.43 1989 5702.63 9957.58 5304.78 6492.43 6630.80 7349.62 8176.62 8573.17 1990 5814.58 12421.25 6369.77 7609.12 7224.75 8121.22 7979.25 8093.06 1991 6470.23 9638.77 8821.17 8722.37 10209.48 11276.55 12552.22 11637.39 1992 9849.69 14558.40 11587.33 9332.56 13082.09 16732.78 19888.61 23933.38 1993 11266.88 21826.84 17357.33 15997.79 18601.53 26155.15 28586.52 30505.41 Sep Oct Nov Dec 1987 6423.48 7600.60 19756.21 2499.81 1988 5835.10 12600.08 28541.72 4717.02 1989 9690.50 15151.84 34061.01 5921.10 1990 8476.70 17914.66 30114.41 4826.64 1991 13606.89 21822.11 45060.69 7615.03 1992 25391.35 36024.80 80721.71 10243.24 1993 30821.33 46634.38 104660.67 In the plot below we can see how the data has changed with time. A seasonal variation can be seen in this graph. Here we can see that sales seems to increase by the time the year ends and is pretty much constant in the initial months of the year.
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> plot.ts(souvenirtimeseries) > logsouvenirtimeseries <- log(souvenirtimeseries) > plot.ts(logsouvenirtimeseries) IN the plot below we can see that not only the seasonal changes are constant but also the fluctuations are constant through out the year.
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