As mentioned in section 12.5, one way to detect autocorrelation is to plot the residuals in time order. If a positive autocorrelation effect is present, there will be clusters of residuals with the same sign and you will readily detect an apparent pattern. If negative autocorrelation exists, residuals will tend to jump back and forth from positive to negative to positive, and so on. This type of pattern is very rarely seen in regression analysis. Thus, the focus of this section is on positive autocorrelation. To illustrate positive autocorrelation, consider the following example. The manager of a package delivery store wants to predict weekly sales based on the number of customers making purchases for a period of 15 weeks. In this situation, because data are collected over a period of 15 consecutive weeks at the same store, you need to determine whether autocorrelation is present. Table 12.4 summarizes the data for this store custSALE. Figure 12.17 illustrates Excel output and Figure 12.18 illustrates Minitab output