國立虎尾科技大學 |

Data analysis using regression and multilevel/hierarchical models /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Data analysis using regression and multilevel/hierarchical models // Andrew Gelman, Jennifer Hill.
其他題名:	Data Analysis Using Regression & Multilevel/Hierarchical Models
作者:	Gelman, Andrew,
其他作者:	Hill, Jennifer,
面頁冊數:	1 online resource (xxii, 625 pages) :digital, PDF file(s). :
附註:	Title from publisher's bibliographic system (viewed on 18 Jul 2016).
標題:	Multilevel models (Statistics) -
電子資源:	https://doi.org/10.1017/CBO9780511790942
ISBN:	9780511790942 (ebook)

Data analysis using regression and multilevel/hierarchical models /
Gelman, Andrew,

Data analysis using regression and multilevel/hierarchical models /Data Analysis Using Regression & Multilevel/Hierarchical ModelsAndrew Gelman, Jennifer Hill. - 1 online resource (xxii, 625 pages) :digital, PDF file(s). - Analytical Methods for Social Research. - Analytical Methods for Social Research..

Title from publisher's bibliographic system (viewed on 18 Jul 2016).

Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities.

Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.

ISBN: 9780511790942 (ebook)Subjects--Topical Terms:

676820
Multilevel models (Statistics)

LC Class. No.: HA31.3 / .G45 2007

Dewey Class. No.: 519.5/36

Data analysis using regression and multilevel/hierarchical models /
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490 0 $a Analytical Methods for Social Research
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505 0 $a Why? -- Concepts and methods from basic probability and statistics -- Linear regression: the basics -- Linear regression: before and after fitting the model -- Logistic regression -- Generalized linear models -- Simulation for checking statistical procedures and model fits -- Causal inference using regression on the treatment variable -- Causal inference using more advanced models -- Multilevel structures -- Multilevel linear models: the basics -- Multilevel linear models: varying slopes, non-nested models, and other complexities.
505 0 $a Multilevel logistic regression -- Multilevel generalized linear models -- Multilevel modeling Bugs and R: the basics -- Fitting multilevel linear and generalized linear models in Bugs and R -- Likelihood and Bayesian inference and computation -- Debugging and speeding convergence -- Sample size and power calculations -- Understanding and summarizing the fitted models -- Analysis of variance -- Causal inference using multilevel models -- Model checking and comparison -- Missing-data imputation -- Six quick tips to improve your regression modeling -- Statistical graphics for research and presentation -- Software.
520 $a Data Analysis Using Regression and Multilevel/Hierarchical Models, first published in 2007, is a comprehensive manual for the applied researcher who wants to perform data analysis using linear and nonlinear regression and multilevel models. The book introduces a wide variety of models, whilst at the same time instructing the reader in how to fit these models using available software packages. The book illustrates the concepts by working through scores of real data examples that have arisen from the authors' own applied research, with programming codes provided for each one. Topics covered include causal inference, including regression, poststratification, matching, regression discontinuity, and instrumental variables, as well as multilevel logistic regression and missing-data imputation. Practical tips regarding building, fitting, and understanding are provided throughout.
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