By Gerald Knight
Total it is a strong booklet, 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 publication is worthy .
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Extra info for Analyzing Business Data with Excel
Change to a customer-based approach 43 44 Figure 2-19. 7. Drilling Down 44 45 Pivot tables have a built-in drill down feature. Suppose we need to see the details for one of the rows in the report from Figure 2-21. The third row for the St. Paul office is customer 160645, and their average order age is 118 days. To see the detail, I double click on the number 118 as shown in Figure 2-22. Figure 2-20. The top five customers by city This adds a new sheet to my workbook containing the orders for this customer.
This starts the pivot table wizard and brings up the dialog in Figure 2-2. For this example we simply click the Finish button. This accepts all of the defaults, and this is often all you will need. Several options are available at this point, including linking our pivot table to external data or making a PivotChart. For now, just click Finish, which brings us to the display in Figure 2-3. A newly created pivot table has a few parts. Item 1 is a list of data items in the table. We can drag and drop these items onto the pivot table.
This value is used in headings and labels. Confidence Level (B4) The confidence level determines the probability used to set the range for the adjusted prediction. Anomaly Detection (B5) The application calculates the probability that the actual value for the day is an anomaly. This user-set value is used to decide if the current value should be ignored. HourlyDist (E2:K12) This area contains the expected work distribution during the day. It uses an 11-hour workday, but any number of divisions is possible.