By Geoff Der
The authors lined many themes in utilized facts, yet they did not point out something approximately time sequence research. i'm disillusioned after analyzing this e-book. the most important challenge with this booklet is that it truly is overly simplistic - usually just one strategy is illustrated for every subject - for instance, in cluster research, merely hierarchical clustering was once pointed out and there has been not anything approximately partitional set of rules. The authors basically used very small datasets, which neglected the most important energy of SAS, the power to deal with huge datasets. The authors additionally revealed all uncooked datasets within the publication, which took quite a lot of space.
The authors should still learn Venables and Ripley's smooth utilized information with SPlus first. Venables/Ripley made an outstanding instance on easy methods to write an utilized records publication utilizing a particular software program.
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Additional info for A handbook of statistical analyses using SAS
One useful feature of ODS is the ability to save procedure output as SAS data sets. Prior to ODS, SAS procedures had a limited ability to save output — parameter estimates, fitted values, residuals, etc. — in SAS data sets, using the out= option on the proc statement, or the output statement. ODS extends this ability to the full range of procedure output. Each procedure's output is broken down into a set of tables and one of these can be saved to a SAS data set by including a statement of the form ods output table = dataset; within the proc step that generates the output.
The histogram statement produces histograms for both variables and the /normal option requests a normal distribution curve. Curves for various other distributions, including nonparametric kernel density estimates (see Silverman ) can be produced by varying this option. Probability plots are requested with the probplot statement. Normal probability plots are the default. 7. 3 provide significant information about the distributions of the two variables, mortality and hardness. Much of this is selfexplanatory, for example, Mean, Std Deviation, Variance, and N.
ODS extends this ability to the full range of procedure output. Each procedure's output is broken down into a set of tables and one of these can be saved to a SAS data set by including a statement of the form ods output table = dataset; within the proc step that generates the output. Information on the tables created by each procedure is given in the “Details” section of the procedure’s documentation. To find the variable names, use proc contents data=dataset; or proc print if the data set is small.
A handbook of statistical analyses using SAS by Geoff Der