The aim of this textbook (previously titled;) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzi...
详细信息
ISBN:
(数字)9783319692395
ISBN:
(纸本)9783319692388
The aim of this textbook (previously titled;) is to teach the use of SAS for statistical analysis of data for advanced undergraduate and graduate students in statistics, data science, and disciplines involving analyzing data.;The book begins with an introduction beyond the basics of SAS, illustrated with non-trivial, real-world, worked examples. It proceeds to SAS programming and applications, SAS graphics, statistical analysis of regression models, analysis of variance models, analysis of variance with random and mixed effects models, and then takes the discussion beyond regression and analysis of variance to conclude.;Pedagogically, the authors introduce theory and methodological basis topic by topic, present a problem as an application, followed by a SAS analysis of the data provided and a discussion of results. The text focuses on applied statistical problems and methods. Key features include: end of chapter exercises, downloadable SAScode and data sets, and advanced material suitable for a second course in applied statistics with every method explained using SAS analysis to illustrate a real-world problem.;New to this edition:;• Covers SAS v9.2 and incorporates new commands;• Uses SAS ODS (output delivery system) for reproduction of tables and graphics output;• Presents new commands needed to produce ODS output;• All chapters rewritten for clarity;• New and updated examples throughout;• All SAS outputs are new and updated, including graphics;• More exercises and problems;• Completely new chapter on analysis of nonlinear and generalized linear models;• Completely new appendix;, is Associate Professor Emeritus of statistics at Iowa State University, where he has taught courses in statistical methods and statistical computing.;, is University Professor of statistics at Iowa State University, where he teaches courses in statistical methodology at both graduate and undergraduate levels and primarily uses SAS to supplement his teachin
Before writing the graphics for SYSTAT in the 1980's, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a pac...
详细信息
ISBN:
(数字)9781475731002
Before writing the graphics for SYSTAT in the 1980's, I began by teaching a seminar in statistical graphics and collecting as many different quantitative graphics as I could find. I was determined to produce a package that could draw every statistical graphic I had ever seen. The structure of the program was a collection of procedures named after the basic graph types they p- duced. The graphics code was roughly one and a half megabytes in size. In the early 1990's, I redesigned the SYSTAT graphics package using - ject-based technology. I intended to produce a more comprehensive and - namic package. I accomplished this by embedding graphical elements in a tree structure. Rendering graphics was done by walking the tree and editing worked by adding and deleting nodes. The code size fell to under a megabyte. In the late 1990's, I collaborated with Dan Rope at the Bureau of Labor statistics and Dan Carr at George Mason University to produce a graphics p- duction library called GPL, this time in Java. Our goal was to develop graphics components. This book was nourished by that project. So far, the GPL code size is under half a megabyte.
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as sup...
详细信息
ISBN:
(数字)9783319964225
ISBN:
(纸本)9783319964201
This book presents the statistical analysis of compositional data using the log-ratio approach. It includes a wide range of classical and robust statistical methods adapted for compositional data analysis, such as supervised and unsupervised methods like PCA, correlation analysis, classification and regression. In addition, it considers special data structures like high-dimensional compositions and compositional tables. The methodology introduced is also frequently compared to methods which ignore the specific nature of compositional data. It focuses on practical aspects of compositional data analysis rather than on detailed theoretical derivations, thus issues like graphical visualization and preprocessing (treatment of missing values, zeros, outliers and similar artifacts) form an important part of the book. Since it is primarily intended for researchers and students from applied fields like geochemistry, chemometrics, biology and natural sciences, economics, and social sciences, all the proposed methods are accompanied by worked-out examples in R using the package robCompositions.
暂无评论