Analysis of Variance, Design, and Regression Linear Modeling for Unbalanced Data, Second Edition
Analysis of Variance, Design, and Regression Linear Modeling for Unbalanced Data, Second Edition by Ronald Christensen is published by CRC Press LLC on December 18, 2020. This comprehensive volume spans 636 pages and is presented in English. The book focuses on linear modeling techniques specifically tailored for unbalanced data, emphasizing the integration of hypotheses into linear models. It provides a detailed analysis of small data sets while also offering tools applicable to larger data contexts.
Readers will find a thorough exploration of various statistical methods, including nonparametric and lasso regression, as well as general additive models. The second edition has been reorganized to enhance the focus on unbalanced data and includes reworked balanced analyses. Additionally, it examines homologous factors and unbalanced split plot analyses, making it suitable for graduate courses in regression and ANOVA or data analysis for advanced statistics students. The text also emphasizes the interpretation of computer output related to unbalanced data, providing practical insights for researchers in mathematics, psychology, and social sciences.
Official synopsis Publisher
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, Second Edition presents linear structures for modeling data with an emphasis on how to incorporate specific ideas (hypotheses) about the structure of the data into a linear model for the data. The book carefully analyzes small data sets by using tools that are easily scaled to big data. The tools also apply to small relevant data sets that are extracted from big data.
New to the Second Edition
- Reorganized to focus on unbalanced data
- Reworked balanced analyses using methods for unbalanced data
- Introductions to nonparametric and lasso regression
- Introductions to general additive and generalized additive models
- Examination of homologous factors
- Unbalanced split plot analyses
- Extensions to generalized linear models
- R, Minitab®, and SAS code on the author’s website
The text can be used in a variety of courses, including a yearlong graduate course on regression and ANOVA or a data analysis course for upper-division statistics students and graduate students from other fields. It places a strong emphasis on interpreting the range of computer output encountered when dealing with unbalanced data.
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