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- Autorius: Andrew Gelman
- Leidėjas: Cambridge University Press
- ISBN-10: 052168689X
- ISBN-13: 9780521686891
- Formatas: 17.5 x 25.2 x 3.6 cm, minkšti viršeliai
- Kalba: Anglų
- Extra -25 % nuolaida šiai knygai su kodu: ENG25
Data Analysis Using Regression and Multilevel Hierarchical Models (el. knyga) (skaityta knyga) | knygos.lt
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Aprašymas
Data Analysis Using Regression and Multilevel/Hierarchical Models 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. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/
EXTRA 25 % nuolaida su kodu: ENG25
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- Autorius: Andrew Gelman
- Leidėjas: Cambridge University Press
- ISBN-10: 052168689X
- ISBN-13: 9780521686891
- Formatas: 17.5 x 25.2 x 3.6 cm, minkšti viršeliai
- Kalba: Anglų Anglų
Data Analysis Using Regression and Multilevel/Hierarchical Models 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. Author resource page: http: //www.stat.columbia.edu/ gelman/arm/
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