An indispensable guide for statistical programmers in the pharmaceutical industry. Statistical programmers in the pharmaceutical industry need to create standardized clinical data using rules created and governed by the Clinical Data Interchange Standards Consortium (CDISC). Thi…
An indispensable guide for statistical programmers in the pharmaceutical industry. Statistical programmers in the pharmaceutical industry need to create standardized clinical data using rules created and governed by the Clinical Data Interchange Standards Consortium (CDISC). Thi…
AI framework intended to solve a problem of bias-variance tradeoff for supervised learning methods in real-life applications. The AI framework comprises of bootstrapping to create multiple training and testing data sets with various characteristics, design and analysis of statis…
Praise for the First Edition:""By placing the R and SAS solutions together and by covering a vast array of tasks in one book, Kleinman and Horton have added surprising value and searchability to the information in their book. ... a home run, and it is a book I am grateful to hav…
An Up-to-Date, All-in-One Resource for Using SAS and R to Perform Frequent TasksThe first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Stat…
Eliminating the need to consult laborious documentation, this book presents an easy way to learn how to perform an analytical task in both SAS and R. It covers many common tasks, along with more complex applications.
An up-to-date, all-in-one resource for using SAS and R to perform frequent tasks The first edition of this popular guide provided a path between SAS and R using an easy-to-understand, dictionary-like approach. Retaining the same accessible format, SAS and R: Data Management, Sta…
Practical time series analysis using SAS
by Milhøj, Anders, SAS Institute
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