# Question

Consider the variance of observations that are m periods apart; that is, Vm = V(yt+m – yt). A graph of Vm/V1 versus m is called a variogram. It is a nice way to check a data series for nonstationary (drifting mean) behavior. If a data series is completely uncorrelated (white noise) the variogram will always produce a plot that stays near unity. If the data series is autocorrelated but stationary, the plot of the variogram will increase for a while, but as m increases the plot of Vm/V1 will gradually stabilize and not increase any further. The plot of Vm/V1 versus m will increase without bound for nonstationary data. Apply this technique to the data in Table 12.1. Is there an indication of nonstationary behavior? Calculate the sample autocorrelation function for the data.

Compare the interpretation of both graphs.

Compare the interpretation of both graphs.

## Answer to relevant Questions

Consider the data shown in Table 12E.1. The target value for this process is 200. (a) Set up an integral controller for this process. Assume that the gain for the adjustment variable is g = 1.2 and assume that = 0.2 in ...Show how a 25 experiment could be set up in two blocks of 16 runs each. Specifically, which runs would be made in each block? One of the variables in the experiment described in Exercise 13.19, heat treatment method (C), is a categorical variable. Assume that the remaining factors are continuous. (a) Write two regression models for predicting crack ...Consider the leaf spring experiment in Exercise 14.9. Rework this problem, assuming that factors A, B, and C are easy to control but factors D and E are hard to control. Using equations (14.6) and (14.7), the mean and ...Sometimes experimenters prefer to use a spherical central composite design in which the axial distance is a = √k , where k is the number of design factors. Is the spherical design similar to the rotatable design? Are there ...Post your question

0