Data Management and Statistical Analysis Techniques
Author : Ronin Myers
Publisher : Scientific e-Resources
Page : 299 pages
File Size : 10,23 MB
Release : 2019-05-19
Category :
ISBN : 1839473398
Author : Ronin Myers
Publisher : Scientific e-Resources
Page : 299 pages
File Size : 10,23 MB
Release : 2019-05-19
Category :
ISBN : 1839473398
Author : Hubert Gatignon
Publisher : Springer Science & Business Media
Page : 396 pages
File Size : 19,24 MB
Release : 2010-01-08
Category : Business & Economics
ISBN : 1441912703
Statistical Analysis of Management Data provides a comprehensive approach to multivariate statistical analyses that are important for researchers in all fields of management, including finance, production, accounting, marketing, strategy, technology, and human resources. This book is especially designed to provide doctoral students with a theoretical knowledge of the concepts underlying the most important multivariate techniques and an overview of actual applications. It offers a clear, succinct exposition of each technique with emphasis on when each technique is appropriate and how to use it. This second edition, fully revised, updated, and expanded, reflects the most current evolution in the methods for data analysis in management and the social sciences. In particular, it places a greater emphasis on measurement models, and includes new chapters and sections on: confirmatory factor analysis canonical correlation analysis cluster analysis analysis of covariance structure multi-group confirmatory factor analysis and analysis of covariance structures. Featuring numerous examples, the book may serve as an advanced text or as a resource for applied researchers in industry who want to understand the foundations of the methods and to learn how they can be applied using widely available statistical software.
Author : Ken Kleinman
Publisher : CRC Press
Page : 325 pages
File Size : 30,32 MB
Release : 2009-07-21
Category : Mathematics
ISBN : 1420070592
An All-in-One Resource for Using SAS and R to Carry out Common TasksProvides a path between languages that is easier than reading complete documentationSAS and R: Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in both SAS and R, without having to navigate through the extensive, id
Author : Ken Kleinman
Publisher : CRC Press
Page : 473 pages
File Size : 48,44 MB
Release : 2014-07-17
Category : Mathematics
ISBN : 1466584491
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, Statistical Analysis, and Graphics, Second Edition explains how to easily perform an analytical task in both SAS and R, without having to navigate through the extensive, idiosyncratic, and sometimes unwieldy software documentation. The book covers many common tasks, such as data management, descriptive summaries, inferential procedures, regression analysis, and graphics, along with more complex applications. New to the Second Edition This edition now covers RStudio, a powerful and easy-to-use interface for R. It incorporates a number of additional topics, including using application program interfaces (APIs), accessing data through database management systems, using reproducible analysis tools, and statistical analysis with Markov chain Monte Carlo (MCMC) methods and finite mixture models. It also includes extended examples of simulations and many new examples. Enables Easy Mobility between the Two Systems Through the extensive indexing and cross-referencing, users can directly find and implement the material they need. SAS users can look up tasks in the SAS index and then find the associated R code while R users can benefit from the R index in a similar manner. Numerous example analyses demonstrate the code in action and facilitate further exploration. The datasets and code are available for download on the book’s website.
Author : Nicholas J. Horton
Publisher : CRC Press
Page : 280 pages
File Size : 37,37 MB
Release : 2015-03-10
Category : Mathematics
ISBN : 1482237377
This book covers the aspects of R most often used by statistical analysts. Incorporating the use of RStudio and the latest R packages, this second edition offers new chapters on simulation, special topics, and case studies. It reorganizes and enhances the chapters on data input and output, data management, statistical and mathematical functions, programming, high-level graphics plots, and the customization of plots. It also provides a detailed discussion of the philosophy and use of the knitr and markdown packages for R.
Author : Nicholas J. Horton
Publisher : CRC Press
Page : 299 pages
File Size : 19,22 MB
Release : 2010-07-28
Category : Mathematics
ISBN : 1439827567
Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsUsing R for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in R, without having to navigate through the extensive, idiosyncratic, and sometimes
Author : Ken Kleinman
Publisher : CRC Press
Page : 308 pages
File Size : 35,64 MB
Release : 2010-07-28
Category : Mathematics
ISBN : 1439827583
Quick and Easy Access to Key Elements of Documentation Includes worked examples across a wide variety of applications, tasks, and graphicsA unique companion for statistical coders, Using SAS for Data Management, Statistical Analysis, and Graphics presents an easy way to learn how to perform an analytical task in SAS, without having to navigate thro
Author : Arlene Fink
Publisher : SAGE
Page : 156 pages
File Size : 47,30 MB
Release : 2003
Category : Medical
ISBN : 9780761925767
Shows how to manage survey data and become better users of statistical and qualitative survey information. This book explains the basic vocabulary of data management and statistics, and demonstrates the principles and logic behind the selection and interpretation of commonly used statistical and qualitative methods to analyze survey data.
Author : M. Jason Hinek
Publisher : CRC Press
Page : 272 pages
File Size : 42,16 MB
Release : 2009-07-21
Category : Computers
ISBN : 1420075195
Thirty years after RSA was first publicized, it remains an active research area. Although several good surveys exist, they are either slightly outdated or only focus on one type of attack. Offering an updated look at this field, Cryptanalysis of RSA and Its Variants presents the best known mathematical attacks on RSA and its main variants, includin
Author : Hulin Wu
Publisher : CRC Press
Page : 329 pages
File Size : 32,1 MB
Release : 2020-12-09
Category : Business & Economics
ISBN : 1000260941
The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.