May 1, 2024
Updated May 11, 2025
26 minute read
Regression models are a fundamental concept in statistics and machine learning, providing a powerful way to understand and predict relationships between variables. At its core, regression analysis seeks to determine how a dependent variable (the outcome you want to predict or understand) is influenced by one or more independent variables (the factors believed to affect the outcome). This might sound abstract, but the applications are incredibly diverse, ranging from forecasting economic trends to identifying risk factors for diseases. For those new to the field, envision trying to predict how much a plant will grow based on the amount of sunlight and water it receives; regression models provide a mathematical way to formalize this kind of relationship.
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Reading list
We've selected 14 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
Regression Models.
Provides a comprehensive overview of statistical learning. It covers a wide range of topics, including regression, classification, and clustering.
Provides a comprehensive overview of regression analysis, covering both linear and nonlinear models. It valuable resource for students and practitioners who want to learn more about regression analysis.
Provides a unique perspective on regression analysis. It covers a wide range of topics, including Bayesian regression, model selection, and causal inference.
Provides a comprehensive overview of machine learning. It covers a wide range of topics, including regression, classification, and clustering.
Provides a comprehensive overview of deep learning. It covers a wide range of topics, including neural networks, convolutional neural networks, and recurrent neural networks.
Classic textbook on generalized linear models. It provides a comprehensive overview of the theory and applications of GLMs.
Classic textbook on regression analysis. It is written in a clear and concise style, and it covers a wide range of topics, including linear regression, logistic regression, and survival analysis.
Provides a comprehensive overview of nonlinear regression. It covers a wide range of topics, including model selection, parameter estimation, and hypothesis testing.
Provides a comprehensive overview of regression analysis for social sciences. It covers a wide range of topics, including linear regression, logistic regression, and structural equation modeling.
Provides a comprehensive overview of regression models in finance. It covers a wide range of topics, including linear regression, time series analysis, and forecasting.
Provides a detailed treatment of regression models for time series analysis. It covers a wide range of topics, including stationarity, autocorrelation, and forecasting.
Provides a practical introduction to regression analysis. It covers a wide range of topics, including data exploration, model building, and prediction.
Provides a comprehensive overview of regression analysis in R. It covers a wide range of topics, including linear regression, logistic regression, and generalized linear models.
Provides a comprehensive overview of multiple linear regression. It covers a wide range of topics, including the Gauss-Markov theorem, hypothesis testing, and confidence intervals.
For more information about how these books relate to this course, visit:
OpenCourser.com/topic/iawg4p/regression
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