Applied Statistics with Python
Volume II: Multivariate Models
Seiten
2025
CRC Press (Verlag)
978-1-041-00625-1 (ISBN)
CRC Press (Verlag)
978-1-041-00625-1 (ISBN)
- Noch nicht erschienen (ca. Dezember 2025)
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This book focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods,clustering, and principal component analysis.
Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.
As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process.
This book is based on the author’s 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required - core concepts are introduced and explained along the way, making the material accessible to a wide range of learners.
Key Features:
Employs Python as an organic part of the learning process
Removes the tedium of hand/calculator computations
Weaves code into the text at every step in a clear and accessible way
Covers advanced machine-learning topics
Uses tools from Standardized sklearn Python package
Applied Statistics with Python, Volume II focuses on ANOVA, multivariate models such as multiple regression, model selection, and reduction techniques, regularization methods like lasso and ridge, logistic regression, K-nearest neighbors (KNN), support vector classifiers, nonlinear models, tree-based methods, clustering, and principal component analysis.
As in Volume I, the Python programming language is used throughout due to its flexibility and widespread adoption in data science and machine learning. The book relies heavily on tools from the standard sklearn package, which are integrated directly into the discussion. Unlike many other resources, Python is not treated as an add-on, but as an organic part of the learning process.
This book is based on the author’s 15 years of experience teaching statistics and is designed for undergraduate and first-year graduate students in fields such as business, economics, biology, social sciences, and natural sciences. However, more advanced students and professionals might also find it valuable. While some familiarity with basic statistics is helpful, it is not required - core concepts are introduced and explained along the way, making the material accessible to a wide range of learners.
Key Features:
Employs Python as an organic part of the learning process
Removes the tedium of hand/calculator computations
Weaves code into the text at every step in a clear and accessible way
Covers advanced machine-learning topics
Uses tools from Standardized sklearn Python package
Leon Kaganovskiy is an Associate Professor at the Mathematics Department of Touro College. He received a M.S. in Theoretical Physics from Kharkov State University, and M.S. and PhD in Applied Mathematics from the University of Michigan. His most recent interest is in a broad field of Applied Statistics, and he has developed new courses in Bio-Statistics with R, Statistics for Actuaries with R, and Business Analytics with R. He teaches Statistics research courses at the Graduate Program in Speech-Language Pathology at Touro College.
Preface 1 Analysis of Variance (ANOVA) 2 Multivariate Data Models 3 Nonlinear Models 4 Tree-Based Methods 5 Unsupervised Models (Principal Values and Clusters) Bibliography Index
| Erscheint lt. Verlag | 30.12.2025 |
|---|---|
| Zusatzinfo | 9 Tables, black and white; 175 Line drawings, color; 175 Illustrations, color |
| Verlagsort | London |
| Sprache | englisch |
| Maße | 156 x 234 mm |
| Themenwelt | Mathematik / Informatik ► Mathematik ► Computerprogramme / Computeralgebra |
| Mathematik / Informatik ► Mathematik ► Statistik | |
| ISBN-10 | 1-041-00625-X / 104100625X |
| ISBN-13 | 978-1-041-00625-1 / 9781041006251 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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