Free delivery for purchases over 1 299 Kč

PPL Parcel Shop 54 Kč Czech Post 74 Kč Balíkovna 49 Kč GLS point 54 Kč Zásilkovna 44 Kč GLS courier 74 Kč PPL courier 99 Kč

Contact

How to shop

Help

My account

▸ Empty :-(

Data Analysis Using Regression and Multilevel/Hierarchical Models

Name: Data Analysis Using Regression and Multilevel/Hierarchical Models
Brand: Cambridge University Press
SKU: 04379008
Price: 3666 CZK
Availability: InStock

Andrew Gelman

Language

English

Book Hardback

Libristo code: 04379008

Publishers Cambridge University Press, December 2006

Data Analysis Using Regression and Multilevel/Hierarchical Models is a comprehensive manual for the... Full description

Libristo code: 04379008

367 b

3 666 Kč včetně DPH

Low in stock at our supplier Shipping in 3-5 days

30-day return policy

Customers also purchased

Fitting Models to Biological Data Using Linear and Nonlinear Regression Harvey Motulsky

/ Paperback

2 759 Kč

Web Application Development with R Using Shiny - Chris Beeley

/ Paperback

1 149 Kč

Predictive Microbiology in Foods Fernando Pérez-Rodríguez

/ Paperback

1 676 Kč

Statistical Tools for Nonlinear Regression Sylvie Huet

/ Paperback

1 540 Kč

Web Application Development with R Using Shiny Chris Beeley

/ Paperback

1 023 Kč

Modeling in Food Microbiology Jeanne-Marie MembrĂ©

/ Hardback

2 006 Kč

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.