Description
Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, 2nd Edition, (PDF) delves into the intricate world of linear models, providing readers with a robust framework for analyzing data. This edition emphasizes the integration of specific hypotheses about the underlying structure of data into linear models, ensuring comprehensive insights. The book adeptly evaluates both small and large datasets, utilizing scalable tools that are also applicable to relevant subsets derived from extensive big data scenarios.
What’s New in the 2nd Edition?
- Introduced methodologies for unbalanced split-plot analyses
- In-depth examination of homologous factors to enhance understanding
- Extended discussions on generalized linear models, catering to advanced applications
- Reorganized content prioritizing the intricacies of unbalanced data analysis
- New sections covering nonparametric methods and lasso regression techniques
- Code snippets for R, Minitab®, and SAS available on the author’s dedicated website
- Introductions to general additive models (GAM) and generalized additive models
- Balanced analyses have been revised using innovative methods suitable for unbalanced datasets
This essential text serves as a foundational resource for a variety of courses, including ANOVA and advanced data analysis programs for upper-level statistics students, as well as comprehensive graduate courses on regression analysis. It places a significant emphasis on accurately interpreting computer-generated outputs when faced with unbalanced data scenarios.
What Readers are Saying
“… articulated in a clear and engaging manner … a superb choice for a beginner-level graduate textbook focusing on statistical methodologies … a valuable reference for practitioners in the field.” ― Zentralblatt für Mathematik
“Tailored primarily for students, each chapter contains illustrative examples and practical exercises. The book’s greatest strength lies in its provision of traditional statistical tools that pave the way for modern approaches in big data. As the author aptly notes, machine learning techniques are fundamentally rooted in classical statistical methods.” ― Marina Gorunescu (Craiova)
NOTE: This product consists of the ebook, Analysis of Variance, Design, and Regression: Linear Modeling for Unbalanced Data, 2nd Edition, available in PDF format. Please note that no access codes are included.
Reviews
There are no reviews yet.