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# hw2_sol - AMS 316 HW2 Solution Sales 50 1995 100 150 200...

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AMS 316 HW2 Solution Sales 1995 1996 1997 1998 1999 50 100 150 200 250 300 Figure 1: 1.(a) 1. (a) R code: data<-c(153, 189, 221, 215, 302, 223, 201, 173, 121, 106, 86, 87, 108, 133, 177, 241, 228, 283, 255, 238, 164, 128, 108, 87, 74, 95, 145, 200, 187, 201, 292, 220, 233, 172, 119, 81, 65, 76, 74, 111, 170, 243, 178, 248, 202, 163, 139, 120, 96, 95, 53, 94) data dat<-ts(data, start=c(1995,1), frequency=13) dat plot(dat, xlab="", ylab="Sales") (b) The solution to this question depends on individual opinion. Stu- dents who argue for the existence of trend and seasonal effect can use various ways to assess them. A simple method is to calculate the four yearly averages in 1995, 1996, 1997 and 1998; and also the average sales in each of periods I,II,...,XIII(i.e. calculate the row and column averages). The yearly averages provide a crude estimates of trend, while the differences between the period averages and the over- all average estimate the seasonal effects. With such a small down- ward trend, this rather crude procedure may well be adequate for most purposes. It has the advantage of being easy to understand and compute. A more sophisticated approach would be to calculate

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