Analyzing Categorical Data:
Gespeichert in:
| 1. Verfasser: | |
|---|---|
| Format: | Elektronisch E-Book |
| Sprache: | English |
| Veröffentlicht: |
New York, NY
Springer New York
2003
|
| Schriftenreihe: | Springer Texts in Statistics
|
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Beschreibung: | Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models. All methods are illustrated with analyses of real data examples, many from recent subject area journal articles. These analyses are highlighted in the text, and are more detailed than is typical, providing discussion of the context and background of the problem, model checking, and scientific implications. More than 200 exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: textbook@springer-ny.com. Jeffrey S. Simonoff is Professor of Statistics at New York University. He is author of Smoothing Methods in Statistics and coauthor of A Casebook for a First Course in Statistics and Data Analysis, as well as numerous articles in scholarly journals. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute |
| Beschreibung: | 1 Online-Ressource (XVI, 498 p) |
| ISBN: | 9780387217277 9781441918376 |
| ISSN: | 1431-875X |
| DOI: | 10.1007/978-0-387-21727-7 |
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| spelling | Simonoff, Jeffrey S. Verfasser aut Analyzing Categorical Data by Jeffrey S. Simonoff New York, NY Springer New York 2003 1 Online-Ressource (XVI, 498 p) txt rdacontent c rdamedia cr rdacarrier Springer Texts in Statistics 1431-875X Categorical data arise often in many fields, including biometrics, economics, management, manufacturing, marketing, psychology, and sociology. This book provides an introduction to the analysis of such data. The coverage is broad, using the loglinear Poisson regression model and logistic binomial regression models as the primary engines for methodology. Topics covered include count regression models, such as Poisson, negative binomial, zero-inflated, and zero-truncated models; loglinear models for two-dimensional and multidimensional contingency tables, including for square tables and tables with ordered categories; and regression models for two-category (binary) and multiple-category target variables, such as logistic and proportional odds models. All methods are illustrated with analyses of real data examples, many from recent subject area journal articles. These analyses are highlighted in the text, and are more detailed than is typical, providing discussion of the context and background of the problem, model checking, and scientific implications. More than 200 exercises are provided, many also based on recent subject area literature. Data sets and computer code are available at a web site devoted to the text. Adopters of this book may request a solutions manual from: textbook@springer-ny.com. Jeffrey S. Simonoff is Professor of Statistics at New York University. He is author of Smoothing Methods in Statistics and coauthor of A Casebook for a First Course in Statistics and Data Analysis, as well as numerous articles in scholarly journals. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and an Elected Member of the International Statistical Institute Statistics Mathematical statistics Economics / Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Kategoriale Daten (DE-588)4327512-6 gnd rswk-swf Datenanalyse (DE-588)4123037-1 gnd rswk-swf Kategoriale Daten (DE-588)4327512-6 s Datenanalyse (DE-588)4123037-1 s 1\p DE-604 https://doi.org/10.1007/978-0-387-21727-7 Verlag Volltext 1\p cgwrk 20201028 DE-101 https://d-nb.info/provenance/plan#cgwrk |
| spellingShingle | Simonoff, Jeffrey S. Analyzing Categorical Data Statistics Mathematical statistics Economics / Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Kategoriale Daten (DE-588)4327512-6 gnd Datenanalyse (DE-588)4123037-1 gnd |
| subject_GND | (DE-588)4327512-6 (DE-588)4123037-1 |
| title | Analyzing Categorical Data |
| title_auth | Analyzing Categorical Data |
| title_exact_search | Analyzing Categorical Data |
| title_full | Analyzing Categorical Data by Jeffrey S. Simonoff |
| title_fullStr | Analyzing Categorical Data by Jeffrey S. Simonoff |
| title_full_unstemmed | Analyzing Categorical Data by Jeffrey S. Simonoff |
| title_short | Analyzing Categorical Data |
| title_sort | analyzing categorical data |
| topic | Statistics Mathematical statistics Economics / Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Kategoriale Daten (DE-588)4327512-6 gnd Datenanalyse (DE-588)4123037-1 gnd |
| topic_facet | Statistics Mathematical statistics Economics / Statistics Statistical Theory and Methods Statistics for Business/Economics/Mathematical Finance/Insurance Statistics for Social Science, Behavorial Science, Education, Public Policy, and Law Statistik Wirtschaft Kategoriale Daten Datenanalyse |
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