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Titlebook: Logistic Regression; A Self-Learning Text David G. Kleinbaum,Mitchel Klein Textbook 2010Latest edition Springer Science+Business Media, LLC

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書目名稱Logistic Regression
副標(biāo)題A Self-Learning Text
編輯David G. Kleinbaum,Mitchel Klein
視頻videohttp://file.papertrans.cn/589/588245/588245.mp4
概述Includes supplementary material:
叢書名稱Statistics for Biology and Health
圖書封面Titlebook: Logistic Regression; A Self-Learning Text David G. Kleinbaum,Mitchel Klein Textbook 2010Latest edition Springer Science+Business Media, LLC
描述This is the third edition of this text on logistic regression methods, originally published in 1994, with its second e- tion published in 2002. As in the first two editions, each chapter contains a pres- tation of its topic in “l(fā)ecture?book” format together with objectives, an outline, key formulae, practice exercises, and a test. The “l(fā)ecture book” has a sequence of illust- tions, formulae, or summary statements in the left column of each page and a script (i. e. , text) in the right column. This format allows you to read the script in conjunction with the illustrations and formulae that highlight the main points, formulae, or examples being presented. This third edition has expanded the second edition by adding three new chapters and a modified computer appendix. We have also expanded our overview of mod- ing strategy guidelines in Chap. 6 to consider causal d- grams. The three new chapters are as follows: Chapter 8: Additional Modeling Strategy Issues Chapter 9: Assessing Goodness of Fit for Logistic Regression Chapter 10: Assessing Discriminatory Performance of a Binary Logistic Model: ROC Curves In adding these three chapters, we have moved Chaps. 8 through 13 from the second
出版日期Textbook 2010Latest edition
關(guān)鍵詞Computerassistierte Detektion; Likelihood; Logistic Regression; SAS; SPSS; Statistical Inference; best fit
版次3
doihttps://doi.org/10.1007/978-1-4419-1742-3
isbn_softcover978-1-4939-3697-7
isbn_ebook978-1-4419-1742-3Series ISSN 1431-8776 Series E-ISSN 2197-5671
issn_series 1431-8776
copyrightSpringer Science+Business Media, LLC 2010
The information of publication is updating

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Analysis of Matched Data Using Logistic Regression,stratification to carry out a matched analysis. Our primary focus is on case-control studies. We then introduce the logistic model for matched data and describe the corresponding odds ratio formula. We illustrate the use of logistic regression with an application that involves matching as well as co
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Polytomous Logistic Regression,n is used when the categories of the outcome variable are nominal, that is, they do not have any natural order. When the categories of the outcome variable do have a natural order, ordinal logistic regression may also be appropriate.
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Logistic Regression for Correlated Data: GEE,d for modeling this type of data is the generalized estimating equations (GEE) model, which takes into account the correlated nature of the responses. If such correlations are ignored in the modeling process, then incorrect inferences may result.
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GEE Examples,dds ratios, construct confidence intervals, and perform statistical tests on the regression coefficients. The examples also illustrate the effect of selecting different correlation structures for a GEE model applied to the same data, and compare the results from the GEE approach with a standard logi
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