Regression analysis classical statistical di james (12 risultati)
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Da: Majestic Books, Hounslow, Regno UnitoMajestic Books
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Da: Books Puddle, New York, NY, U.S.A.Books Puddle
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Da: Biblios, frankfurt am main, HESSE, GermaniaBiblios
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Da: California Books, Miami, FL, U.S.A.California Books
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Da: PBShop.store US, Wood Dale, IL, U.S.A.PBShop.store US
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: PBShop.store UK, Fairford, GLOS, Regno UnitoPBShop.store UK
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EUR 60,72
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PAP. Condizione: New. New Book. Shipped from UK. Established seller since 2000.
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Da: Ria Christie Collections, Uxbridge, Regno UnitoRia Christie Collections
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Da: BargainBookStores, Grand Rapids, MI, U.S.A.BargainBookStores
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EUR 77,80
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Paperback or Softback. Condizione: New. Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python. Book.
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Da: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, GermaniaBuchWeltWeit Ludwig Meier e.K.
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EUR 73,20
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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression… modeling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning. 502 pp. Englisch.
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Da: buchversandmimpf2000, Emtmannsberg, BAYE, Germaniabuchversandmimpf2000
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EUR 73,20
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Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression mod…eling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 502 pp. Englisch.
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Da: preigu, Osnabrück, Germaniapreigu
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EUR 65,35
EUR 70,00 spedizioneSpedito da Germania a U.S.A.Quantità: 5 disponibili
Taschenbuch. Condizione: Neu. Regression Analysis with Classical and Statistical Learning Methods | An Easy Guide for Data Scientists, Business Analysts and Engineers using Python | K C James | Taschenbuch | Englisch | 2025 | Academic Enclave | EAN 9789348642516 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 362…44 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.
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Da: AHA-BUCH GmbH, Einbeck, GermaniaAHA-BUCH GmbH
Contatta il venditoreVenditore con 5 stelleCondizione: Nuovo
EUR 78,08
EUR 64,68 spedizioneSpedito da Germania a U.S.A.Quantità: 1 disponibili
Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Regression is a powerful technique in data analysis for modeling relationships between variables, making it crucial for prediction, decision-making, and pattern recognition. This book offers an accessible introduction to regression mode…ling, tailored for postgraduate students in fields such as data science, engineering, statistics, mathematics, business, and the sciences. It simplifies complex mathematical concepts and emphasizes real-world applications, complemented by coding examples to reinforce key concepts.The book covers classical regression methods including simple and multiple linear regression, polynomial regression, and logistic regression. It also addresses regression diagnostics, such as model evaluation, outlier detection, and assessment of model assumptions. By integrating classical methods with modern machine learning techniques, it offers a unique perspective. Machine learning techniques like support vector regression, decision trees, and artificial neural networks (ANN) for regression tasks are introduced, demonstrating their complementarity to classical methods through practical examples. The book also explores advanced methods such as Ridge, Lasso, Elastic Net, Principal Component Regression, and Generalized Linear Models (GLMs). These techniques are demonstrated using Python libraries like Statsmodels and Scikit-learn, enabling students to engage in practical learning.
