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An R Companion to Applied Regression is a broad introduction to the R statistical computing environment in the context of applied;
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methodology covered includes statistical standard distributions, one- and two-sample tests with continuous data, regression analysis, one-and two-way;
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Build effective regression models in R to extract valuable insights from real data Key Features Implement different regression analysis;
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Analysis with Applications in R, Second Edition is a comprehensive and up-to-date guide to conducting complex regressions in the R statistical;
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Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;
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Advanced Regression Models with SAS and R exposes the reader to the modern world of regression analysis. The material covered by this book;
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This book describes a comprehensive regression analysis to the conduct of scientific research. It outlines theoretical principals;
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estimates.Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate and upper-level;
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of parameter estimates. Complete with an extensive bibliography, Regression Analysis and Its Application is suitable for statisticians, graduate;
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analysis, regression, statistical distributions, and multivariate statistics. Full of examples and with a color insert, it helps readers become;
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This is today's most complete guide to regression analysis with Microsoft (R) Excel for any business analytics or research task. Drawing on;
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This set contains: 9780471469438 Regression and ANOVA: An Integrated Approach Using SAS(R) Software by Keith E. Muller, Bethel A. Fetterman;
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It is no exaggeration to say that virtually all quantitative research in the social sciences is done with correlation and regression;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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Simultaneous confidence bands enable more intuitive and detailed inference of regression analysis than the standard inferential methods;
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expanded both in popularity and in the number of packages available. Extending the Linear Model with R: Generalized Linear, Mixed Effects and;
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shows how to use these Gaussian process regression models in the analysis of functional data. Some MATLAB (R) and C codes are available on the;
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An outstanding introduction to the fundamentals of regression analysis-updated and expanded The methods of regression analysis are the most;
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- Coherent and unified treatment of nonlinear regression with R. - Example-based approach. - Wide area of application.;
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can be carried out with most of the currently available statistical software packages, such as the software package R. Regression Analysis by;
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This book serves as an accessible introduction into how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered;
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This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data;
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This book is an introduction to regression analysis, focusing on the practicalities of doing regression analysis on real-life data;
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Discover what you can do with R! Introducing the R system, covering standard regression methods, then tackling more advanced topics, this;
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regression analysis and diagnostics without supervision. Featuring a wealth of real-world examples, exercises, and codes in R, the book;
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models. Subsequent chapters balance essential concepts with SAS(R) tools and cover key topics such as: Regression analysis using Proc IML and;
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application area that benefits from regression analysis can also benefit from semiparametric regression. Assuming only a basic familiarity with;
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