categorical data

Concise, complete, nontechnical—the ideal introduction to an increasingly important topic In recent years, the use of statistical methods for categorical data has increased dramatically in a variety of areas and applications. This book provides an applied introduction to the most important methods for analyzing categorical data. It summarizes methods that have long played a prominent role, such as chi-squared tests, but places special emphasis on logistic regression and loglinear modeling techniques. ...

In this fun-filled book, playful puns and comical cartoon cats combine to show, not tell, readers what prepositions are all about. Each preposition in the text, like under, over, by the clover, about, throughout, and next to Rover, is highlighted in color for easy identification. ...

"What's a Contraction? You'll find the answer inside this book?it's brimming with contractions! Brian P. Cleary's playful rhymes and Brian Gable's humorous illustrations don't shy away from creatively clarifying the concept of contractions. For easy identification, we've printed the contractions in color. You won't want to miss the comical cats that reinforce each idea. ...

One book is never enough to explore the wide range of verbs! The crazy cats deliver loads of additional examples to illustrate the power of both action verbs and linking verbs. Brian P. Cleary's playful verse and Brian Gable's comical cats turn traditional grammar lessons on end. Each verb is printed in color for easy identification. Read this book aloud and share the delight of the sense and nonsense of words.

One book is never enough to explore the wide range of verbs! The crazy cats deliver loads of additional examples to illustrate the power of both action verbs and linking verbs. Brian P. Cleary's playful verse and Brian Gable's comical cats turn traditional grammar lessons on end. Each verb is printed in color for easy identification. Read this book aloud and share the delight of the sense and nonsense of words.

SAS/STAT software provides extensive statistical capabilities with tools for both specialized and enterprise-wide analytical needs. It can help you analyze data and make informed decisions for research, engineering, manufacturing, medical, and business applications. This user's guide provides the latest, detailed reference material for the procedures in SAS/STAT, including analysis of variance, regression, categorical data analysis, multivariate analysis, survival analysis, psychometric analysis, cluster analysis, nonparametric analysis, and survey data analysis. Also included are syntax and usage information, examples, and a discussion of the use of the Output Delivery System with the statistical software. New chapters for SAS 9.1 describe software for power and sample size computations, robust regression, multiple imputation, crosstabulations, table analysis, and logistic regression for survey data. Many other chapters have been updated with SAS 9.1 enhancements. This title serves as a reference guide for both novice and expert users of SAS/STAT software. ...

Using parallel machines is difficult because of their inherent complexity and because their architecture changes frequently. This book presents an integrated approach to developing software for parallel machines that addresses software issues and performance issues together. The author describes a methodology for software construction that is architecture-independent and intellectually abstract. The software can execute efficiently on a range of existing and potential hardware configurations. The approach is based on the construction of categorical data types, a generalization of abstract data types, and of objects. This work will be an outstanding reference for researchers in computing. ...

Using real-world data examples, this authoritative book shows how to use the latest configural frequency analysis (CFA) techniques to analyze categorical data. Some of the techniques are presented here for the first time. In contrast to methods that focus on relationships among variables, such as log-linear modeling, CFA allows researchers to evaluate differences and change at the level of individual cells in a table. Illustrated are ways to identify and test for cell configurations that are either consistent with or contrary to hypothesized patterns (the types and antitypes of CFA); control for potential covariates that might influence observed results; develop innovative prediction models; address questions of moderation and mediation; and analyze intensive longitudinal data. The book also describes free software applications for executing CFA.

Regression Analysis for Social Sciences presents methods of regression analysis in an accessible way, with each method having illustrations and examples. A broad spectrum of methods are included: multiple categorical predictors, methods for curvilinear regression, and methods for symmetric regression. This book can be used for courses in regression analysis at the advanced undergraduate and beginning graduate level in the social and behavioral sciences. Most of the techniques are explained step-by-step enabling students and researchers to analyze their own data. Examples include data from the social and behavioral sciences as well as biology, making the book useful for readers with biological and biometrical backgrounds. Sample command and result files for SYSTAT are included in the text. ...

Data Preparation for Data Mining addresses an issue unfortunately ignored by most authorities on data mining: data preparation. Thanks largely to its perceived difficulty, data preparation has traditionally taken a backseat to the more alluring question of how best to extract meaningful knowledge. But without adequate preparation of your data, the return on the resources invested in mining is certain to be disappointing.

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Are you a data mining analyst, who spends up to 80% of your time assuring data quality, then preparing that data for developing and deploying predictive models? And do you find lots of literature on data mining theory and concepts, but when it comes to practical advice on developing good mining views find little how to information? And are you, like most analysts, preparing the data in SAS?