tsa solution manual
Solutions Manual to Accompany
Time Series Analysis with Applications in R, Second Edition by Jonathan D. Cryer and Kung-Sik Chan
Solutions by Jonathan Cryer and Xuemiao Hao, updated 7/28/08
CHAPTER 1
Exercise 1.1 Use software to produce the time series plot shown in Exhibit (1.2), page 2. The following R code will produce the graph.
> library(TSA); data(larain); win.graph(width=3,height=3,pointsize=8)
> plot(y=larain,x=zlag(larain),ylab='Inches',xlab='Previous Year Inches')
Exercise 1.2 Produce the time series plot displayed in Exhibit (1.3), page 3. Use the R code
> data(color); plot(color,ylab='Color Property',xlab='Batch',type='o')
Exercise 1.3 Simulate a completely random process of length 48 with independent, normal values. Repeat this exercise several times with a new simulation, that is, a new seed, each time.
> plot(ts(rnorm(n=48)),type='o') # If you repeat this command R will use a new “random numbers” each time. If you want to reproduce the same simulation first use the command set.seed(#########) where ######### is an integer of your choice.
Exercise 1.4 Simulate a completely random process of length 48 with independent, chi-square distributed values each with 2 degrees of freedom. Use the same R code as in the solution of Exercise 1.3 but replace rnorm(n=48) with rchisq(n=48,df=2).
Exercise 1.5 Simulate a completely random process of length 48 with independent, t-distributed values each with 5 degrees of freedom. Construct the time series plot. Use the same R code as in the solution of Exercise 1.3 but replace rnorm(n=48) with rt(n=48,df=5).
Exercise 1.6 Construct a time series plot with monthly plotting symbols for the Dubuque temperature series as in
Exhibit (1.7), page 6. (Make the plot full screen so that you can see all of detail.)
> data(tempdub); plot(tempdub,ylab='Temperature')
> points(y=tempdub,x=time(tempdub), pch=as.vector(season(tempdub)))
CHAPTER 2
Exercise 2.1 Suppose E(X) = 2, Var(X) = 9, E(Y) = 0, Var(Y) = 4, and Corr(X,Y) = 0.25.
Time Series Analysis with Applications in R, Second Edition by Jonathan D. Cryer and Kung-Sik Chan
Solutions by Jonathan Cryer and Xuemiao Hao, updated 7/28/08
CHAPTER 1
Exercise 1.1 Use software to produce the time series plot shown in Exhibit (1.2), page 2. The following R code will produce the graph.
> library(TSA); data(larain); win.graph(width=3,height=3,pointsize=8)
> plot(y=larain,x=zlag(larain),ylab='Inches',xlab='Previous Year Inches')
Exercise 1.2 Produce the time series plot displayed in Exhibit (1.3), page 3. Use the R code
> data(color); plot(color,ylab='Color Property',xlab='Batch',type='o')
Exercise 1.3 Simulate a completely random process of length 48 with independent, normal values. Repeat this exercise several times with a new simulation, that is, a new seed, each time.
> plot(ts(rnorm(n=48)),type='o') # If you repeat this command R will use a new “random numbers” each time. If you want to reproduce the same simulation first use the command set.seed(#########) where ######### is an integer of your choice.
Exercise 1.4 Simulate a completely random process of length 48 with independent, chi-square distributed values each with 2 degrees of freedom. Use the same R code as in the solution of Exercise 1.3 but replace rnorm(n=48) with rchisq(n=48,df=2).
Exercise 1.5 Simulate a completely random process of length 48 with independent, t-distributed values each with 5 degrees of freedom. Construct the time series plot. Use the same R code as in the solution of Exercise 1.3 but replace rnorm(n=48) with rt(n=48,df=5).
Exercise 1.6 Construct a time series plot with monthly plotting symbols for the Dubuque temperature series as in
Exhibit (1.7), page 6. (Make the plot full screen so that you can see all of detail.)
> data(tempdub); plot(tempdub,ylab='Temperature')
> points(y=tempdub,x=time(tempdub), pch=as.vector(season(tempdub)))
CHAPTER 2
Exercise 2.1 Suppose E(X) = 2, Var(X) = 9, E(Y) = 0, Var(Y) = 4, and Corr(X,Y) = 0.25.