regression analysis classical statistical (18 résultats)

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PAP. Etat : New. New Book. Shipped from UK. Established seller since 2000.

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Paperback or Softback. Etat : New. Regression Analysis with Classical and Statistical Learning Methods: An Easy Guide for Data Scientists, Business Analysts and Engineers using Python. Book.

Paperback. Etat : New.

Paperback. Etat : New.

LeatherBound. Etat : New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. Leatherbound edition. Condition: New. Leather Binding on Spine and Corners with Golden leaf printing on spine. Bound in genuine leather with Satin ribbon page markers and Spine with raised gilt bands. Pages: 44. A perfect gift for your loved ones. Reprinted from 1978 edition. NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. IF YOU WISH TO ORDER PARTICULAR VOLUME OR ALL THE VOLUMES YOU CAN CONTACT US. Resized as per current standards. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Language: English Pages: 44.

LeatherBound. Etat : New. BOOKS ARE EXEMPT FROM IMPORT DUTIES AND TARIFFS; NO EXTRA CHARGES APPLY. LeatherBound edition. Condition: New. Reprinted from 1978 edition. Leather Binding on Spine and Corners with Golden leaf printing on spine. NO changes have been made to the original text. This is NOT a retyped or an ocr'd reprint. Illustrations, Index, if any, are included in black and white. Each page is checked manually before printing. Pages: 52 As this print on demand book is reprinted from a very old book, there could be some missing or flawed pages, but we always try to make the book as complete as possible. Fold-outs, if any, are not part of the book. If the original book was published in multiple volumes then this reprint is of only one volume, not the whole set. Sewing binding for longer life, where the book block is actually sewn (smythe sewn/section sewn) with thread before binding which results in a more durable type of binding. Pages: 52 Language: English.

Taschenbuch. Etat : 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.

Taschenbuch. Etat : 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 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.Libri GmbH, Europaallee 1, 36244 Bad Hersfeld 502 pp. Englisch.

Taschenbuch. Etat : 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, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.

Taschenbuch. Etat : 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 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.