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Applied Statistics with PythonApplied 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

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The supplementary electronic material (available online through Springer Link) contains recipes that include all the MATLAB commands featured in the book and example data

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