Data Analysis Using Regression and Multilevel/Hierarchical Models
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| Produkt | Varenr. | Pris | Handling |
|---|---|---|---|
| Data Analysis Using Regression and Multilevel/Hierarchical Models | 9780521686891 | 494,95 kr | Til butik |
| Data Analysis Using Regression and Multilevel/Hierarchical Models | 9780521686891 | 494,95 kr | Til butik |
Produktdetaljer
Data Analysis Using Regression and Multilevel/Hierarchical Models
494,95 kr
Til butik- Varenr.:
- 9780521686891
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.
Læs mere hos Saxo DKData Analysis Using Regression and Multilevel/Hierarchical Models
494,95 kr
Til butik- Varenr.:
- 9780521686891
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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