Top 10 Linear Regression Books
Welcome to our roundup of the top books on Linear Regression! Whether you’re a data analyst looking to broaden your knowledge or a seasoned expert diving deeper into the subject, we’ve curated a selection of titles to help you grow your understanding of this essential statistical method.
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1. Linear Models in Statistics
Alvin C. Rencher and G. Bruce Schaalje’s book, Linear Models in Statistics, Second Edition, serves as a vital introduction to the principles and applications of linear model theory for those making strides in the field of statistics. As most advanced statistical tools originate from linear models, comprehending this foundational concept is crucial for growth and advancement.
Through its comprehensive and meticulously updated content, this book offers a strong grasp of linear models in regression, analysis of variance, analysis of covariance, and linear mixed models. The authors touch upon recent advancements in methodology like mixed, generalized linear models, and Bayesian linear model methods. Furthermore, they delve into more intricate topics including geometry of least squares, vector-matrix calculus, simultaneous inference, logistic regression, and nonlinear regression.
In the revised edition, readers are introduced to advanced Bayesian linear models and random/mixed linear models. Additionally, the book covers enhanced discussions on two-way models with empty cells. The book also includes updated information on simultaneous inference and the geometry of least squares.
Rencher and Schaalje present a well-organized theoretical framework that enables readers to effectively interpret computer software output. This resource is invaluable for upper-undergraduate and graduate students in statistics, biostatistics, and mathematics, as well as researchers seeking to deepen their understanding of regression and analysis of variance.
Linear Models in Statistics, Second Edition features straightforward proofs, real-world data sets, a detailed bibliography, a comprehensive review of matrix algebra, and an extensive collection of theoretical and applied problems with selected answers. The book is accompanied by a related website that provides additional data sets and SAS® code for all numerical examples.
- Authors: Alvin C. Rencher, G. Bruce Schaalje
- Publisher: John Wiley & Sons
- Published Date: January 18, 2008
- Page Count: 688
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
2. Linear Regression Analysis
Theory and Computing
Dive into the depths of linear regression analysis with “Linear Regression Analysis: Theory and Computing” by Xin Yan. This comprehensive tome explores the fundamental theories of linear regression and offers a practical guide to diagnosing and modeling data-all while leveraging the power of SAS.
Aimed at graduate students in statistics or biostatistics, this engaging book provides clear explanations of key concepts and cutting-edge techniques. By following this guide, readers will gain valuable insights into the world of linear regression and prepare themselves for future research in the field.
With a staggering 349 pages packed with invaluable knowledge, “Linear Regression Analysis: Theory and Computing” has become an indispensable resource for those seeking to master the art of linear regression. Don’t miss out on this exceptional journey into the realm of statistical computing and the future of linear regression analysis.
- Authors: Xin Yan
- Publisher: World Scientific
- Published Date: March 30, 2009
- Page Count: 349
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
3. Sensitivity Analysis in Linear Regression
“Sensitivity Analysis in Linear Regression” is a comprehensive guide that bridges the gap between linear regression theory and real-life applications. Authored by Samprit Chatterjee and Ali S. Hadi, this John Wiley & Sons publication delves into the diagnosis of linear regression as a crucial component for successful model implementation.
With 315 pages, the book offers an in-depth exploration of linear regression diagnostics, employing a wealth of examples to fortify the theory. It further delves into the consequences of measurement errors on the estimated coefficients. This often-overlooked aspect is paramount when using regression coefficients to allocate effects associated with distinct variables.
Moreover, “Sensitivity Analysis in Linear Regression” assesses the robustness of the regression fit both qualitatively and numerically. The authors demonstrate how this crucial step can ultimately strengthen the application of linear regression in real-world scenarios.
Whether you’re a seasoned mathematician or embarking on your regression journey, this enlightening guide aims to enhance your understanding and facilitate your journey. Dive into the world of regression diagnostics and harness its potential with “Sensitivity Analysis in Linear Regression. “
- Authors: Samprit Chatterjee, Ali S. Hadi
- Publisher: John Wiley & Sons
- Published Date: September 25, 2009
- Page Count: 315
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
4. Linear Regression Analysis
Embark on a comprehensive journey through the world of linear regression analysis with the revised and expanded edition of “Linear Regression Analysis” by authors George A. F. Seber and Alan J. Lee. This second edition offers extensive coverage of the latest theories and applications, making this text a go-to resource for students and professionals alike.
Written with a clear and concise mathematical approach, this book delves into the intricacies of line regression analysis while requiring no specialized knowledge beyond a basic understanding of matrix algebra. With over 200 problems dispersed throughout the text, alongside outline solutions for the exercises, readers can expect a robust and engaging learning experience.
Additionally, “Linear Regression Analysis” is class-tested and highly regarded in the academic community. As a result, it has become a staple resource for those looking to enhance their understanding and practical application of linear regression analysis.
- Authors: George A. F. Seber, Alan J. Lee
- Publisher: John Wiley & Sons
- Published Date: January 20, 2012
- Page Count: 584
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
5. Linear Regression
“Linear Regression” by Jürgen Groß is a comprehensive guide to understanding the fundamental concepts and applications of linear regression models. This authoritative book delves into the basic theory of linear regression, exploring different estimation techniques that serve as alternatives and complements to least squares estimation. The author provides detailed proofs for the most relevant results and illustrates the presented methods through numerical examples, graphics, and practical applications.
In addition, the book offers an introduction to the basics of decision theory and an appendix on matrix algebra, further enhancing its scope and depth. With its user-friendly approach and extensive coverage, “Linear Regression” is an essential resource for students, researchers, and professionals working in the field of mathematics and data analysis. Whether you are new to the subject or seeking to deepen your knowledge, this engaging and informative book will undoubtedly prove invaluable.
- Authors: Jürgen Groß
- Publisher: Springer Science & Business Media
- Published Date: December 06, 2012
- Page Count: 398
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
6. Learning Statistics with R
“Discovering the World of Statistics with R” is a comprehensive and user-friendly guidebook that seamlessly merges the elements of statistical learning and undergraduate psychology. Written by Daniel Navarro, this publication is designed for readers who are eager to grasp the fundamentals of R-based statistics in a conversational and lighthearted atmosphere.
The book begins with an in-depth introduction to R and provides illustrative guidance on how to manipulate data and create scripts. Starting with an exploration of descriptive statistics and graphing, the book gradually introduces readers to complex topics such as probability theory, sampling and estimation, and null hypothesis testing.
In addition, this book delves into the intricacies of analyzing contingency tables, conducting t-tests, utilizing ANOVAs, and employing regression. The concluding chapters focus on Bayesian statistics, making “Discovering the World of Statistics with R” a complete book that offers a well-rounded study of R-based statistics.
Navarro’s emphasis on a strong correlation between theory and practical application makes “Discovering the World of Statistics with R” an essential book for undergraduate psychology students. Moreover, the book’s extensive coverage of R-based statistics makes it a valuable resource for practitioners who aim to enhance their statistical analysis skills.
For a more detailed synopsis or a sneak peek into the book, please visit http: //ua. edu. au/ccs/teaching/lsr or http: //learningstatisticswithr. com. With a total of 617 pages, this book is not just a page-turner, but a turning point for your journey into statistical expertise.
- Authors: Daniel Navarro
- Publisher: Lulu.com
- Published Date: January 13, 2013
- Page Count: 617
- Print Type: BOOK
- Categories: Psychology
- Maturity Rating: NOT_MATURE
- Language: en
7. Applied Linear Regression
Dive into the world of linear regression with “Applied Linear Regression” by Sanford Weisberg. This masterfully revised third edition is your go-to guide for mastering the essentials and advanced techniques of linear regression modeling. Weisberg’s comprehensive approach covers model building, assessing fit and reliability, and drawing conclusions through estimation, confidence, and testing procedures.
“Applied Linear Regression” includes a wealth of new material reflecting the latest advances in the field, such as utilizing smoothers for summarizing scatterplots, Box-Cox transformations, and employing the delta method for inference about complex combinations of parameters. The book also explores computationally intensive methods, simulation, and the bootstrap method. In addition, the text features expanded chapters on nonlinear and logistic regression, as well as completely revised chapters on multiple regression, diagnostics, and generalizations of regression.
Enhancing your learning experience is a collection of over 100 exercises, most of which are based on real-world data, coupled with web primers demonstrating how to use standard statistical packages like R, S-Plus, SPSS, SAS, and JMP to work through all the examples and exercises. Furthermore, a free online library for R and S-Plus simplifies the implementation of the methods discussed in the book.
“Applied Linear Regression” is an invaluable resource for both upper-level undergraduate and graduate students, offering a balanced approach of graphical methods, analysis, and practical examples to ensure a quick grasp of linear regression techniques and their application in solving real-life problems. Get ready to unlock the power of linear regression with this must-have textbook!
- Authors: Sanford Weisberg
- Publisher: John Wiley & Sons
- Published Date: June 07, 2013
- Page Count: 331
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
8. Introduction to Linear Regression Analysis
Embark on an enlightening journey with “Introduction to Linear Regression Analysis, “ a comprehensive and up-to-date guide to the fundamentals of regression analysis. Written by the esteemed authors Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining, this fifth edition serves as a practical resource for both students and professionals alike.
Blending theory and application, the book introduces readers to various uses of linear regression in today’s cutting-edge scientific research, equipping them with the essential knowledge for applying regression model-building techniques across diverse fields. From engineering and management to the health sciences, this all-encompassing resource covers it all.
With numerous newly added topics, readers will delve into the intricacies of regression analysis of time series data, regression models with random effects, subsampling, and testing individual regression coefficients and subsets of coefficients. The authors also provide real-life examples of the application of simple linear regression models and multiple regression models for understanding patient satisfaction data.
Incorporating a variety of software packages, such as Minitab, SAS, S-PLUS, JMP, and R, this latest edition ensures readers can easily grasp the discussed techniques and procedures. Thoroughout the book, numerous exercises have been added to test readers’ understanding of the material.
“Introduction to Linear Regression Analysis” is a must-have for statistics and engineering courses at the upper-undergraduate and graduate levels. Professionals in engineering, life and biological sciences, and the social sciences will also find value in this comprehensive resource.
- Authors: Douglas C. Montgomery, Elizabeth A. Peck, G. Geoffrey Vining
- Publisher: John Wiley & Sons
- Published Date: June 29, 2015
- Page Count: 679
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
9. Linear Regression
Explore the world of linear regression and experimental design models in “Linear Regression” by David J. Olive. This comprehensive textbook, published by Springer in 2017, bridges the gap between theory and practice, making it an excellent resource for students with a strong mathematical background.
Discover the essentials of multiple linear regression, response plots for visualizing models and detecting outliers, and prediction intervals that account for unknown error distributions. Learn about bootstrap hypothesis tests for inference after variable selection, as well as the development of prediction regions and large sample theory for multivariate linear regression models.
Delve into the relationship between multivariate prediction regions and confidence regions, which often provide a practical method for testing hypotheses. The book also features a chapter on generalized linear and additive models, enhanced by a multitude of R functions for response and residual plots, simulation, outlier detection, and response transformations.
“Linear Regression” is designed for undergraduates and graduates with a background in linear algebra and calculus-based statistics. Dive into the world of mathematical analysis and statistical modeling with this engaging and enlightening text.
- Authors: David J. Olive
- Publisher: Springer
- Published Date: April 18, 2017
- Page Count: 494
- Print Type: BOOK
- Categories: Mathematics
- Maturity Rating: NOT_MATURE
- Language: en
10. Interpretable Machine Learning
“Unraveling Machine Learning” is an all-encompassing guide on making machine learning models and their choices more comprehensible. Written by Christoph Molnar, this book explores the idea of interpretability and dives into uncomplicated models such as decision trees, decision rules, and linear regression.
The subsequent chapters tackle model-agnostic approaches for interpreting opaque models, including feature importance, accumulated local effects, Shapley values, and LIME. The entire interpretation process is meticulously explained, reviewed, and proven within the text. Readers will develop a strong grasp of the efficacy, drawbacks, and practical interpretations of these techniques for a myriad of machine learning projects.
With 320 pages and a rating of 4.5 from the publisher, this book lays a sturdy groundwork for those aiming to unravel the mysteries of machine learning interpretability. It’s an essential read for individuals interested in the practical implications of artificial intelligence and machine learning in a clear and explicable manner.
- Authors: Christoph Molnar
- Publisher: Lulu.com
- Published Date: May 17, 2020
- Page Count: 320
- Print Type: BOOK
- Categories: Artificial intelligence
- Average Rating: 4.5
- Ratings Count: 2.0
- Maturity Rating: NOT_MATURE
- Language: en
