7 edition of **statistical analysis of discrete data** found in the catalog.

- 134 Want to read
- 14 Currently reading

Published
**1989**
by Springer-Verlag in New York
.

Written in English

- Multivariate analysis

**Edition Notes**

Includes bibliographical references (p. [310]-339) and indexes.

Statement | Thomas J. Santner, Diane E. Duffy. |

Series | Springer texts in statistics |

Contributions | Duffy, Diane E. |

Classifications | |
---|---|

LC Classifications | QA278 .S26 1989 |

The Physical Object | |

Pagination | xii, 367 p. : |

Number of Pages | 367 |

ID Numbers | |

Open Library | OL2211339M |

ISBN 10 | 0387970185 |

LC Control Number | 89034062 |

Discrete Data Analysis with R: Visualization and Modeling techniques for Categorical and Examine a number of recent issues of a scientiﬁc or statistical journal in which you have some interest. Find book, but often based on some data or analysis. Do a Google image search for the topic “Global warming” to see a Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (Chapman & Hall/CRC Texts in Statistical Science Book ) - Kindle edition by Friendly, Michael, Meyer, David. Download it once and read it on your Kindle device, PC, phones or tablets. Use features like bookmarks, note taking and highlighting while reading Discrete Data Analysis with R › Kindle Store › Kindle eBooks › Science & Math.

Categorical Data Analysis, 3rd Edition, Wiley. This is the new and improved text of Agresti (). It is less theoretical and therefore less technical than Agresti (). Students are free to purchase either or text for this course. References are provided in the lesson materials for both :// - Buy Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (Chapman & Hall/CRC Texts in Statistical Science) book online at best prices in India on Read Discrete Data Analysis with R: Visualization and Modeling Techniques for Categorical and Count Data (Chapman & Hall/CRC Texts in Statistical Science) book

This book provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research. An explicit aim of the book is to integrate the transformational and the latent variable approach, two diverse but complementary traditions dealing with the analysis of categorical :// When sample sizes are not large or the data are otherwise sparse, exact methods--methods not based on asymptotic theory--are more accurate and therefore book introduces the statistical theory, analysis methods, and computation techniques for exact analysis of discrete ://

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The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete :// The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data.

The book's mathematical prereq › Statistics. The Statistical Analysis of Discrete Data provides an introduction to current statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data. The book's mathematical prerequisites are linear algebra and elementary advanced :// The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as statistical analysis of discrete data book reference for researchers who analyze discrete data. The book's mathematical prereq uisites are linear algebra and elementary advanced :// The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

Problems are provided at the end of each chapter to give the reader an opportunity to ap ply the methods in the text, to explore extensions of the material covered, and to analyze data with discrete :// The Statistical Analysis of Discrete Data provides an introduction to cur rent statistical methods for analyzing discrete response data.

The book can be used as a course text for graduate students and as a reference for researchers who analyze discrete data.

The book's mathematical prereq uisites are linear algebra and elementary advanced › Books › Science & Math › Mathematics. Statistical Analysis and Data Display An Intermediate Course with Examples in S-Plus, R, and SAS. Authors and as a reference book for researchers. In-depth discussions of regression analysis, analysis of variance, and design of experiments are followed by introductions to analysis of discrete bivariate data, nonparametrics, logistic the doctoral level, and as a reference book for researchers.

In-depth. discussions of regression analysis, analysis of variance, and design. of experiments are followed by introductions to analysis of discrete.

bivariate data, nonparametrics, logistic regression, and ARIMA time. series modeling. The authors illustrate classical concepts and Welcome to STAT – Analysis of Discrete Data.

The focus of this class is a multivariate analysis of discrete data. Here we deal with data which are discretely measured responses such as counts, proportions, nominal variables, ordinal variables, discrete interval variables with few values, continuous variables grouped into a small number of Time series analysis and temporal autoregression Moving averages Trend Analysis ARMA and ARIMA (Box-Jenkins) models Spectral analysis 18 Resources Distribution tables Bibliography Statistical Software Test Datasets and data archives Websites This book introduces the statistical theory, analysis methods, and computation techniques for exact analysis of discrete data.

After reviewing the relevant discrete distributions, the author develops the exact methods from the ground up in a conceptually integrated :// Statistical Methods for Categorical Data Analysis. 2 nd Edition. Daniel A. Powers and Yu Xie. Statistical Methods for Categorical Data Analysis by Daniel A.

Powers and Yu Xie provides a comprehensive introduction to methods and models for categorical data analysis and their applications in social science research.

An explicit aim of the book is to integrate the transformational and the latent Discrete data therefore include the number of customer complaints, or the number of people who like ice cream, i.e.

you cannot have half a complaint, or a third of a person. Another example would be age in whole years. For the purposes of analysis, discrete data are considered very similar to continuous :// About this book.

Introduction. This introductory statistics textbook conveys the essential concepts and tools needed to develop and nurture statistical thinking. data analysis has become an integral part of any scientific study. Issues such as judging the credibility of data, analyzing the data, evaluating the reliability of the obtained Analysis of data obtained from discrete variables requires the use of specific statistical tests which are different from those used to assess continuous variables (such as cardiac output, blood pressure, or PaO 2) which can assume an infinite range of values.

The analysis of continuous variables is discussed in the next The statistical analysis of discrete multivariate data has received a great deal of attention in the statistics literature over the past two decades.

The develop ment ofappropriate models is the common theme of books such as Cox (), Haberman (,), Bishop et al.

(), Gokhale and Kullback (), Upton (), Fienberg Professor C.R. Rao has made great contributions to linear and quadratic estimation in linear models. The classical methods will remain a beautiful and important standard; but they can be extended to incorporate explicitly phenomena such as outliers and gross errors, thereby further narrowing the gap between mathematics and the problems of practical data :// This book is designed for readers with limited background in statistical methodology, who seek guidance in defending their statistical decision making in the worlds of research and practice.

It helps them select data analytic methods, and speak knowledgeably about their statistical research › Statistics. A number of statistical issues arise in the estimation of economic welfare measures from data on CV responses using the maximum likelihood method.

Optimal experimental design procedures are outlined to select the bids to be used in CV surveys. Advanced topics in statistical design and the analysis of discrete choice data are :// 11 The Statistical Analysis of Discrete-Response CV Data MICHAEL HANEMANN AND BARBARA KANNINEN1 1. INTRODUCTION AND OVERVIEW In recent years, increasing attention has been given to the statistical aspects of contingent valuation (CV) survey design and data analysis.

The main reason for the growing interest in statistical issues is. Additional Physical Format: Online version: Aickin, Mikel.

Linear statistical analysis of discrete data. New York: Wiley, © (OCoLC) Structural Analysis of Discrete Data and Econometric Applications. Charles F. Manski and Daniel L. McFadden, Editors Cambridge: The MIT Press, Permission is granted to individuals who wish to copy this book, in whole or in part, for academic instructional or research ://~mcfadden/ Computational Statistics & Data Analysis 16 () North-Holland Analysis of discrete data: Rerandomization methods and complexity Jenny Baglivo Boston College, Chestnut Hill, MAUSA Donald Olivier Harvard University School of Public Health, Boston, MAUSA Marcello Pagano Harvard University School of Public Health and Dana -Farber Cancer Institute, Boston, MA