大葉大學圖書館 |

Modeling binary correlated responses using SAS, SPSS and R[electronic resource] /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	519.5
書名/作者:	Modeling binary correlated responses using SAS, SPSS and R/ by Jeffrey R. Wilson, Kent A. Lorenz.
作者:	Wilson, Jeffrey R.
其他作者:	Lorenz, Kent A.
出版者:	Cham : : Springer International Publishing :, 2015.
面頁冊數:	xxiii, 264 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Statistics.
標題:	Statistical Theory and Methods.
標題:	Biostatistics.
標題:	Statistics for Life Sciences, Medicine, Health Sciences.
ISBN:	9783319238050
ISBN:	9783319238043
內容註:	Introduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model.
摘要、提要註:	Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
電子資源:	http://dx.doi.org/10.1007/978-3-319-23805-0

Modeling binary correlated responses using SAS, SPSS and R[electronic resource] /
Wilson, Jeffrey R.

Modeling binary correlated responses using SAS, SPSS and R[electronic resource] /by Jeffrey R. Wilson, Kent A. Lorenz. - Cham :Springer International Publishing :2015. - xxiii, 264 p. :ill., digital ;24 cm. - ICSA book series in statistics,2199-0980. - ICSA book series in statistics..

Introduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model.

Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.

ISBN: 9783319238050

Standard No.: 10.1007/978-3-319-23805-0doiSubjects--Topical Terms:

145349
Statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Modeling binary correlated responses using SAS, SPSS and R[electronic resource] /
LDR:02668nam a2200325 a 4500 001 444304
003 DE-He213
005 20160415134713.0
006 m d
007 cr nn 008maaau
008 160715s2015 gw s 0 eng d
020 $a 9783319238050 $q (electronic bk.)
020 $a 9783319238043 $q (paper)
024 7 $a 10.1007/978-3-319-23805-0 $2 doi
035 $a 978-3-319-23805-0
040 $a GP $c GP
041 0 $a eng
050 4 $a QA276
072 7 $a PBT $2 bicssc
072 7 $a MAT029000 $2 bisacsh
082 0 4 $a 519.5 $2 23
090 $a QA276 $b .W749 2015
100 1 $a Wilson, Jeffrey R. $3 635737
245 1 0 $a Modeling binary correlated responses using SAS, SPSS and R $h [electronic resource] / $c by Jeffrey R. Wilson, Kent A. Lorenz.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2015.
300 $a xxiii, 264 p. : $b ill., digital ; $c 24 cm.
490 1 $a ICSA book series in statistics, $x 2199-0980
505 0 $a Introduction to Binary logistic Regression -- Growth of the Logistic Regression Model -- Standard Binary Logistic Regression Model -- Overdispersed Logistic Regression Model -- Weighted Logistic Regression Model -- Generalized Estimating Equations Logistic Regression -- Generalized Method of Moments logistic regression Model -- Exact Logistic Regression Model -- Two-Level Nested Logistic Regression Model -- Hierarchical Logistic Regression models -- Fixed Effects Logistic Regression Model -- Heteroscedastic Logistic Regression Model.
520 $a Statistical tools to analyze correlated binary data are spread out in the existing literature. This book makes these tools accessible to practitioners in a single volume. Chapters cover recently developed statistical tools and statistical packages that are tailored to analyzing correlated binary data. The authors showcase both traditional and new methods for application to health-related research. Data and computer programs will be publicly available in order for readers to replicate model development, but learning a new statistical language is not necessary with this book. The inclusion of code for R, SAS, and SPSS allows for easy implementation by readers. For readers interested in learning more about the languages, though, there are short tutorials in the appendix. Accompanying data sets are available for download through the book s website. Data analysis presented in each chapter will provide step-by-step instructions so these new methods can be readily applied to projects. Researchers and graduate students in Statistics, Epidemiology, and Public Health will find this book particularly useful.
650 0 $a Statistics. $3 145349
650 2 4 $a Statistical Theory and Methods. $3 464135
650 2 4 $a Biostatistics. $3 464330
650 2 4 $a Statistics for Life Sciences, Medicine, Health Sciences. $3 464109
700 1 $a Lorenz, Kent A. $3 635738
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a ICSA book series in statistics. $3 634196
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-23805-0
950 $a Mathematics and Statistics (Springer-11649)