Applied Regression Analysis And Other Multivariable Methods(5th Edition)
Authors:
David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, Eli S. Rosenberg
In Stock: 1 Left
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Total Price:
$59.54
Solution Manual Includes
Price:
$9.99
Book details
ISBN: 1285051084, 978-1285963754, 128596375X, 978-1285051086
Book publisher: Cengage Learning
Book Price $59.54 : The book 'Applied Regression Analysis and Other Multivariable Methods', authored by David G. Kleinbaum, Lawrence L. Kupper, Azhar Nizam, and Eli S. Rosenberg, is a detailed guide on regression analysis pivotal for statistical students and practitioners. The fifth edition of this seminal work covers foundational to advanced topics in linear regression analysis, addressing both simple and multiple linear regression models. A hallmark feature of this text is its comprehensive solution manual which provides students with detailed problem solutions, enabling a deep understanding of regression techniques and multivariable methods. Throughout the book, readers encounter a meticulously organized table of contents that guides them through ordinary least squares, logistic regression, and survival analysis, among other techniques. The text includes an answer key to help learners check their understanding. This edition embellishes its content with real-data examples, bringing to life the application of regression analysis in public health, business analytics, and social sciences. Known for its practical applicability mediated by high-quality illustrations and insightful explanations, the book received positive reception for its thoroughness and clarity, making it a staple reference in statistical analysis. A well-curated set of end-of-chapter exercises reinforces learning and assists in practical application, preparing readers for real-world problem-solving. With its in-depth exploration of methodological frameworks, this book stands as an indispensable resource in statistical education, providing pivotal insights into analytical strategies. A cheap, scholarly resource designed to support the learning requirements of diverse student groups.
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