data analysis using regression di gelman andrew (49 risultati)

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Condizione: New. 2006. 1st Edition. paperback. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Series Editor(s): Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. Series: Analytical Methods for Social Research. Num Pages: 648 pages, 160 exercises. BIC Classification: JHBC; PBK. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 179 x 37. Weight in Grams: 1120. Series: Analytical Methods for Social Research. 648 pages, 160 exercises. For the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: JHBC; PBK. Dimension: 254 x 179 x 37. Weight: 1132. Series Editor(s) :Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. . . . . .

Condizione: New. 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. It introduces an.

Paperback. Condizione: Neu. Neu Neuware, Importqualität, auf Lager - 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. It introduces and demonstrates a variety of models and instructs the reader in how to fit these models using freely available software packages.

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paperback. Condizione: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Condizione: New. 2006. 1st Edition. paperback. This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Series Editor(s): Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. Series: Analytical Methods for Social Research. Num Pages: 648 pages, 160 exercises. BIC Classification: JHBC; PBK. Category: (P) Professional & Vocational; (U) Tertiary Education (US: College). Dimension: 254 x 179 x 37. Weight in Grams: 1120. Series: Analytical Methods for Social Research. 648 pages, 160 exercises. For the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. Cateogry: (P) Professional & Vocational; (U) Tertiary Education (US: College). BIC Classification: JHBC; PBK. Dimension: 254 x 179 x 37. Weight: 1132. Series Editor(s) :Alvarez, R. Michael; Beck, Nathaniel L.; Wu, Lawrence L. . . . . . Books ship from the US and Ireland.

Condizione: good. May show signs of wear, highlighting, writing, and previous use. This item may be a former library book with typical markings. No guarantee on products that contain supplements Your satisfaction is 100% guaranteed. Twenty-five year bookseller with shipments to over fifty million happy customers.

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Paperback. Condizione: new. Paperback. 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: / gelman/arm/ 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: ~gelman/arm/ Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

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Paperback. Condizione: new. Paperback. 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: / gelman/arm/ 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: ~gelman/arm/ Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Paperback. Condizione: new. Paperback. 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: / gelman/arm/ 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: ~gelman/arm/ Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Gebunden. Condizione: New. 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. It introduces an.