Books like Applied linear statistical models by Michael H. Kutner


First publish date: 2005
Genre: Education & Language/Education
Authors: Michael H. Kutner
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Applied linear statistical models by Michael H. Kutner

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Books similar to Applied linear statistical models (13 similar books)

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A practical guide to Bayesian statistical analysis that prioritizes computation, model checking, and data‑collection design, with examples spanning regression, hierarchical, robust, generalized linear, and mixture models.

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📘 Applied linear statistical models
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Applied Linear Statistical Models – a comprehensive textbook by John Neter that covers linear models, regression analysis, analysis of variance, and experimental design for statisticians and researchers.

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📘 An Introduction to Statistical Learning

An Introduction to Statistical Learning offers a practitioner‑friendly guide to modern statistical learning methods, including linear regression, classification, resampling, shrinkage, tree‑based models, SVMs, and clustering, with R tutorials and real‑world examples.

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A concise guide for advanced undergraduate and beginning graduate students, introducing the theory of linear statistical models.

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Design of Experiments offers a concise, matrix‑oriented introduction to linear models, balancing breadth with depth for course use and illustrating theory with worked examples.

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Statistical principles in experimental design by 13204494|B. J. Winer book cover

📘 Statistical principles in experimental design

A revision of this classic statistics text for first-year graduate students in psychology, education and related social sciences. The two new authors are former students of Winer's. They have updated, rewritten and reorganized the text to fit the course as it is now taught.

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Linear models by 12009842|S. R. Searle book cover

📘 Linear models


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📘 Linear statistical models


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Methods and applications of linear models by 8855269|R. R. Hocking book cover

📘 Methods and applications of linear models

A popular statistical text, now updated and better than ever!

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