By Geoff Der
The authors coated many themes in utilized statistics, yet they did not point out whatever approximately time sequence research. i'm dissatisfied after analyzing this e-book. the most important challenge with this publication is that it truly is overly simplistic - often just one strategy is illustrated for every subject - for instance, in cluster research, in basic terms hierarchical clustering used to be pointed out and there has been not anything approximately partitional set of rules. The authors merely used very small datasets, which neglected the most important energy of SAS, the power to deal with huge datasets. The authors additionally published all uncooked datasets within the ebook, which took rather a lot of space.
The authors should still learn Venables and Ripley's sleek utilized facts with SPlus first. Venables/Ripley made a superb instance on find out how to write an utilized records e-book utilizing a particular software program.
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Extra info for A handbook of statistical analyses using SAS
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. 3 Deleting Observations It may be necessary to delete observations from the data set, either because they contain errors or because the analysis is to be carried out on a subset of the data.
Thus, symbol1 v=square i=join; symbol2 v=triangle i=join; proc gplot; plot y * x = sex; run; will produce two lines with different plot characters. An alternative would be to remove the plot characters and use different types of line for the two subgroups. The l= (linetype) option of the symbol statement may be used to achieve this; for example, symbol1 v=none i=join l=1; symbol2 v=none i=join l=2; ©2002 CRC Press LLC proc gplot; plot y * x = sex; run; Both of the above examples assume that two symbol definitions are being generated — one by the symbol1 statement and the other by symbol2.
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. This can be achieved using either the subsetting if statement or the where statement. The subsetting if statement consists simply of the keyword if followed by a logical condition. Only observations for which the condition is true are included in the data set being created. data men; set survey; if sex=’M’; run; The statement where sex=’M’; has the same form and could be used to achieve the same effect.