Use software to produce the time series plot shown in Exhibit 1.2, on page 2. The data are in the file named larain.
library(TSA) library(latticeExtra) data(larain, package = "TSA")
xyplot(larain, ylab = "Inches", xlab = "Year", type = "o")
Produce the time series plot displayed in Exhibit 1.3, on page 3. The data file is named color.
data(color) xyplot(color, ylab = "Color property", xlab = "Batch", type = "o")
Simulate a completely random process of length 48 with independent, normal values. Plot the time series plot. Does it look “random”? Repeat this exercise several times with a new simulation each time.
xyplot(as.ts(rnorm(48))) xyplot(as.ts(rnorm(48)))
As far as we can tell there is no discernable pattern here.
Simulate a completely random process of length 48 with independent, chi-square distributed values, each with 2 degrees of freedom. Display the time series plot. Does it look “random” and nonnormal? Repeat this exercise several times with a new simulation each time.
xyplot(as.ts(rchisq(48, 2))) xyplot(as.ts(rchisq(48, 2)))
The process appears random, though non-normal.
Simulate a completely random process of length 48 with independent, t-distributed values each with 5 degrees of freedom. Construct the time series plot. Does it look “random” and nonnormal? Repeat this exercise several times with a new simulation each time.
xyplot(as.ts(rt(48, 5))) xyplot(as.ts(rt(48, 5)))
It looks random but not normal, though it should be approximately so, considering the distribution that we have sampled from.
Construct a time series plot with monthly plotting symbols for the Dubuque temperature series as in Exhibit 1.7, on page 6. The data are in the file named tempdub.
data(tempdub) xyplot(tempdub, ylab = "Temperature", xlab = "Year")
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