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Applied Statistics with Python

Autor Leon Kaganovskiy
en Limba Engleză Paperback – 26 dec 2025
The set focuses on applied and computational statistics, ANOVA, multivariate models like multiple regression, model selection, reduction techniques, regularization methods like lasso, ridge, logistic regression, K-nearest neighbors, support vector classifiers, nonlinear models, tree-based methods, clustering and principal component analysis.
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Specificații

ISBN-13: 9781041191704
ISBN-10: 1041191707
Pagini: 646
Dimensiuni: 156 x 234 mm
Editura: Taylor & Francis Ltd.

Cuprins

Preface  1. Introduction  2. Descriptive Data Analysis  3. Probability  4. Probability Distributions  5. Inferential Statistics and Tests for Proportions  6. Goodness of Fit and Contingency Tables  7. Inference for Means  8. Correlation and Regression

Notă biografică

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.