By Gerald Knight
Total this can be a strong ebook, however it will be a lot nicer in the event that they supplied Excel worksheets with the knowledge utilized in the book's examples. a few pattern code is supplied for dowload on Oreilly's site, yet this doesn't contain instance facts. still, the e-book is worthy .
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Notice that the correlation is high only at five and ten. This confirms that the numbers have a five day cycle. Correlating a list of numbers against itself is called autocorrelation . The procedure is illustrated in Figure 3-1. 2. Find the average The easiest prediction is that each day will have the same volume as the same day in the previous week. But this leads to trouble if last week's call volume was unusual. So, it is better to use a recent average. But even the average can be skewed by a really odd day, and a filtered average tends to do the best job.
Figure 2-5. Changing from Count to Average This allows me to control which offices will appear on the table. Here, I have unchecked (blank) to exclude it. When OK is clicked, the display looks like Figure 2-9. 35 36 There is a big difference in performance. 6 days on average while St. Paul takes over 42 days. Some of the difference might be understandable if there is a difference in the kinds or value of orders between the offices. 3. Multiple Data Items Next we check to see if the average order amount is related to average order age.
On the Settings worksheet the user can enter a value for Confidence Level. The application will give the range for the adjusted prediction at that probability. 9, the application will display the prediction and a +/- range. There is a 90% probability that the actual value will be in that range. In Figure 3-4, the adjusted prediction for the next day is 14,630. There is a 90% probability that the actual value will be 14,630 +/- 305. 1,STDEV(D3:D16-B3:B16),15)}. The CONFIDENCE function returns the confidence interval .