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A "specialized feature" occurs in your experiment if you collect additional measurements beyond the basics needed for the design. For example, you are doing sampling if your experiment involves measuring an experimental unit more than once (sometimes called "subsampling" by scientists). You are using covariate if you are measuring an additional explanatory variable. None means you have no specialized features and have taken no additional measurements. Your RCBD Single Factor analysis will be modified if it includes specialized features, such as Sampling or Covariate (or their combination). In later steps in the module, you must access these choices in gray boxes (like the one at right). To show you how to analyze a RCBD Single Factor experiment, a dataset is needed. You can either use one of the example data sets provided (gray box at right) or your own data. For your first run through a module, it can be helpful to download and use one of the example datasets, so you can see exactly how SAS looks throughout the analysis for your particular specialized features. Then run the module with your own data. If you need help identifying specialized features, click on the yellow box at right or the link below for more information.
Click a choice in gray table at above right to download an example data file. At the prompt, choose Open and the example data will appear in your spreadsheet program.
Decide which specialized feature, if any, you have in your experiment, based on the illustrations and defnitions above. If you wish, examine the example dataset having your specialized features, to see how a spreadsheet file is structured for those features to assure SAS works with it correctly. In particular, notice the variable names in the first row and data values in subsequent rows. SAS uses the standard data file organization, with each column representing a variable and each row representing an observation. In Step 7A, the purpose of each variable (each column) in the spreadsheet will be explained.
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