1. Summarize the results of John's analysis in one paragraph that a manager, not a forecaster, can understand.
2. Describe the trend and seasonal effects that appear to be present in the sales data for Mr. Tux.
3. How would you explain the line "Random 49%."?
4. Consider the significant autocorrelations, r12 and r24, for the differenced data. Would you conclude that the sales first differenced have a seasonal component? If so, what are the implications for forecasting, say, the monthly changes in sales?

John Mosby, owner of several Mr. Tux rental stores, is beginning to forecast his most important business variable, monthly dollar sales (see the Mr. Tux cases at the ends of Chapters 1 and 2). One of his employees, Virginia Perot, has gathered the sales data shown in Case 2-2. John decides to use all 96 months of data he has collected. He runs the data on Minitab and obtains the autocorrelation function shown in Figure 3-25. Since all the autocorrelation coefficients are positive and they are trailing off very slowly, John concludes that his data have a trend.