May 1, 2024
Updated May 9, 2025
20 minute read
Multiple regression is a statistical technique used to understand the relationship between multiple independent (or predictor) variables and a single dependent (or outcome) variable. It aims to model this relationship by fitting a linear equation to observed data. Essentially, it helps us determine how much the dependent variable is expected to change when the independent variables change. This powerful tool is widely used across many fields to make predictions, understand complex relationships among variables, and inform decision-making.
jyboan|
Find a path to becoming a Multiple Regression. Learn more at:
OpenCourser.com/topic/jyboan/multiple
Featured in The Course Notes
This topic is mentioned in our blog,
The Course Notes. Read
one article that features
Multiple Regression:
To read more articles from OpenCourser, visit:
OpenCourser.com/notes
Reading list
We've selected 12 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
Multiple Regression.
Classic text on multiple regression and correlation analysis. It provides a thorough treatment of the topic, and it is written in a clear and accessible style. It is suitable for both students and researchers.
Provides a comprehensive overview of multiple regression, covering both the theoretical and practical aspects of the method. It is written in a clear and concise style, and it is suitable for both students and researchers.
Provides a comprehensive overview of multiple regression and structural equation modeling. It is written in a clear and concise style, and it is suitable for both students and researchers.
Provides a comprehensive overview of causal inference in statistics. It covers a wide range of topics, including multiple regression, structural equation modeling, and graphical models. The authors are leading experts in the field of causal inference.
Provides a comprehensive overview of multiple regression and related statistical methods. It is written in a clear and concise style, and it is suitable for both students and researchers.
Provides a comprehensive overview of statistical methods used in medical research. It covers a wide range of topics, including multiple regression, logistic regression, and survival analysis. The author leading expert in the field of medical statistics.
Provides a comprehensive treatment of generalized linear models, which are a powerful class of statistical models that can be used to model a wide variety of data types. Multiple regression special case of generalized linear models, and this book provides a good foundation for understanding the more general case.
Provides a practical guide to multiple regression. It covers a wide range of topics, including model selection, hypothesis testing, and interpreting results. The author leading expert in the field of multiple regression.
Provides a practical guide to regression analysis. It covers a wide range of topics, including multiple regression, generalized linear models, and time series analysis. The authors are leading experts in the field of regression analysis.
Provides a practical guide to interpretable machine learning. It covers a wide range of topics, including multiple regression, decision trees, and random forests. The author leading expert in the field of interpretable machine learning.
Provides a practical guide to multiple regression using SAS. It covers a wide range of topics, including data preparation, model selection, and interpretation. The authors are leading experts in the field of multiple regression.
Provides a practical introduction to regression modeling, with a focus on applications in actuarial science and finance. It covers a wide range of topics, including multiple regression, generalized linear models, and time series analysis.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/jyboan/multiple
Save
