SAS For Dummies by Stephen McDaniel, Chris Hemedinger

By Stephen McDaniel, Chris Hemedinger

  • Created in partnership with SAS, this ebook explores SAS, a enterprise intelligence software program that may be utilized in any company surroundings or company for information supply, reporting, information mining, forecasting, statistical research, and more
  • SAS worker and technologist Stephen McDaniel combines real-world services and a pleasant writing variety to introduce readers to SAS basics
  • Covers an important issues corresponding to getting quite a few sorts of info into the software program, generating studies, operating with the knowledge, uncomplicated SAS programming, macros, and dealing with SAS and databases

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Filtering SAS data One of the most frequently used features of SAS Enterprise Guide is the Filter and Query task. After you open a data set, this task makes it easy to filter the data to analyze just the records that interest you. Filtering data can be as simple as organizing customer data based on country or the patients in a trial based on year of birth. Filtering can be based on one or many conditions, using “and” or “or” logic, and can even utilize complex formulas in the conditions. ” 27 28 Part I: Welcome to SAS!

From the Heading Properties for Subcategory dialog box that appears, click the General tab, as shown in Figure 2-18, and delete the text in the Label field. Do the same for the row identifier Product and for statistic identifier Sum. Click OK. Figure 2-18: Heading properties for a categorical variable. 11. Back in the Summary Tables pane, click Titles in the left panel. 39 40 Part I: Welcome to SAS! 12. Click the Use Default text box to turn off the default title and enter the following for Text for Section: Regional Sales Summary by Subcategory and Product.

And don’t worry if you don’t know what a p-value is right now. ) The surprising secret of actually arriving at good results for decision-making is the huge amount of time that many people spend accessing, organizing, and preparing their data for a particular analysis. In visiting more than 50 major companies in our various work experiences, we’ve found that the common theme is the massive amount of resource and rework time spent on the data preparation aspect of business analytics. As mentioned earlier in this chapter, the first developers of SAS were doing real-world research projects and faced these very same data preparation and analysis issues.

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