May 1, 2024
Updated May 27, 2025
22 minute read
Diving into the World of Linear Models
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Find a path to becoming a Linear Models. Learn more at:
OpenCourser.com/topic/7mt5f5/linear
Reading list
We've selected 31 books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Linear Models.
Classic text on generalized linear models. It provides a deep dive into the theory and application of these models.
Offers a more accessible introduction to statistical learning compared to 'The Elements of Statistical Learning,' covering linear regression and related topics with practical examples in R. It's an excellent resource for upper undergraduate and beginning graduate students to gain a solid applied understanding of linear models within a modern statistical learning framework.
This foundational graduate-level text in statistical learning that covers linear methods extensively within a broader context of modern statistical techniques. It's a must-read for those interested in the theoretical underpinnings and contemporary applications of linear models in data mining and prediction. It valuable reference for researchers and advanced students.
Offers a hands-on approach to learning and applying linear models using the R programming language. It's particularly useful for students and professionals who want to implement linear modeling techniques. The book bridges the gap between theory and practice, making it valuable for solidifying understanding through application. The third edition incorporates recent developments in R.
Introduces statistical modeling from a Bayesian perspective, including generalized linear multilevel models. It emphasizes conceptual understanding and practical implementation using R and Stan. It's highly relevant for those interested in contemporary Bayesian approaches to linear models and offers a unique perspective compared to traditional frequentist texts.
Offers a unified treatment of both linear models and generalized linear models, balancing theoretical concepts with practical applications in R. It's an excellent resource for understanding the connections and extensions of linear models and is suitable for graduate students and researchers. It provides a solid foundation in both areas.
Provides a comprehensive overview of linear and generalized linear mixed models. It good resource for students who are interested in these models.
Provides a comprehensive overview of linear models, with a focus on using R for data analysis. It covers topics such as simple linear regression, multiple linear regression, and generalized linear models.
This widely recognized and comprehensive textbook covering linear models, including regression, analysis of variance, and experimental design. It's an excellent resource for gaining a broad understanding of applied linear statistical models and is commonly used as a textbook in undergraduate and graduate programs across various disciplines. The book provides numerous examples and emphasizes the practical application and interpretation of results.
This leading and comprehensive text on Bayesian statistical methods, including detailed coverage of Bayesian approaches to linear and generalized linear models. It's essential for those interested in a Bayesian perspective on statistical modeling and valuable reference for researchers and advanced students. It covers a wide range of topics and computational techniques.
Covers the entire process of building predictive models, starting with data preprocessing and covering various techniques, including linear models. It has a strong practical focus with extensive R code and is highly relevant for those applying linear models in data science and predictive analytics. It's a valuable reference for practitioners.
This classic and authoritative theoretical text on generalized linear models (GLMs), which significantly extend the scope of traditional linear models. It is essential for those seeking a deep, theoretical understanding of GLMs and standard reference in the field. is best suited for graduate students and researchers with a strong mathematical background.
Provides a rigorous theoretical treatment of linear models using a vector space approach. It's ideal for graduate students and researchers who want to delve into the mathematical foundations of linear models and ANOVA. It offers a deep understanding of the subject matter and serves as a valuable theoretical reference.
Provides a comprehensive treatment of the design and analysis of experiments, an important application area for linear models (ANOVA type of linear model). It standard textbook in engineering and statistics and is valuable for understanding how linear models are applied to design and analyze experiments for process and product improvement. The 10th edition is recent and incorporates modern approaches.
Provides a solid introduction to linear regression analysis, a fundamental component of linear models. It is well-suited for undergraduate students and those new to the topic, offering a practical approach with a focus on model building, diagnostics, and applications. It serves as a good entry point for understanding how linear models are applied to real-world data.
Provides a comprehensive overview of linear models in German. It good resource for students who are interested in the theory and application of linear models.
Provides a comprehensive overview of linear regression analysis. It good resource for students who are interested in the theory and application of linear models.
Provides a comprehensive and mathematically rigorous treatment of the theory and applications of linear regression analysis. It is suitable for those seeking a deeper theoretical understanding of linear models and is often used in graduate-level statistics courses. It serves as a thorough reference on the subject.
Provides comprehensive coverage of linear models, including regression, ANOVA, and experimental design, with a balanced approach to theory and application. It is suitable for graduate students and offers a solid understanding of the subject matter, serving as a good reference for various linear modeling techniques.
Provides a comprehensive introduction to forecasting methods, many of which are based on linear models, particularly in the context of time series data. It emphasizes practical application using R and is suitable for students and practitioners interested in forecasting. It demonstrates a key application area for linear models.
Offers a critical and insightful examination of statistical models, with a strong focus on the linear model and its applications. It encourages readers to think deeply about model assumptions and causality. It's valuable for solidifying a conceptual understanding of linear models and their limitations, making it a useful companion to more technical texts.
Provides a broad overview of statistical learning methods, including linear models. It good resource for students who are new to the topic.
This classic and comprehensive text on applied regression analysis. It covers a wide range of topics in detail and has been a standard reference for practitioners for many years. While the most recent edition is older, its thorough coverage of foundational applied regression techniques remains valuable.
Focuses on linear mixed-effects models, an important extension of linear models used for analyzing data with hierarchical or repeated measures structures. It provides a practical guide to fitting and interpreting these models using the `lme4` package in R, making it valuable for researchers and practitioners working with such data. It covers a contemporary topic in linear modeling applications.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/7mt5f5/linear
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