By Geoff Der

The authors lined many themes in utilized information, yet they did not point out whatever approximately time sequence research. i'm disillusioned after interpreting this publication. the largest challenge with this publication is that it truly is overly simplistic - generally just one method is illustrated for every subject - for instance, in cluster research, purely hierarchical clustering used to be pointed out and there has been not anything approximately partitional set of rules. The authors in simple terms used very small datasets, which overlooked the most important strength of SAS, the power to address huge datasets. The authors additionally revealed all uncooked datasets within the ebook, which took quite a lot of space.

The authors may still learn Venables and Ripley's smooth utilized records with SPlus first. Venables/Ripley made an exceptional instance on tips on how to write an utilized data e-book utilizing a selected software program.

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**Extra info for A handbook of statistical analyses using SAS**

**Example text**

Starting from the data statement, a typical data step will read in some data with an input or set statement and use that data to construct an observation. The observation will then be used to execute the statements that follow. The data in the observation can be modified or added to in the process. At the end of the data step, the observation will be written to the data set being created. The sequence will begin again from the data statement, reading the data for the next observation, processing it, and writing it to the output data set.

Deleting erroneous observations is best done using the if then statement with the delete statement. if weightloss > startweight then delete; ©2002 CRC Press LLC In a case like this, it would also be useful to write out a message giving more information about the observation that contains the error. if weightloss > startweight then do; put 'Error in weight data' idno= startweight= weightloss=; delete; end; The put statement writes text (in quotes) and the values of variables to the log. 4 Subsetting Data Sets If analysis of a subset of the data is needed, it is often convenient to create a new data set containing only the relevant observations.

However, the array only lasts for the duration of the data step in which it is defined. 2 Deleting Variables Variables can be removed from the data set being created by using the drop or keep statements. The drop statement names a list of variables that are to be excluded from the data set, and the keep statement does the converse, that is, it names a list of variables that are to be the only ones retained in the data set, all others being excluded. So the statement drop x y z; in a data step results in a data set that does not contain the variables x, y, and z, whereas keep x y z; results in a data set that contains only those three variables.