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Everything about Linear regression
Statistical modeling method
In statistics, linear regression is a model that estimates the relationship between a scalar response and one or more explanatory variables related via a linear combination. A linear model with exactly one explanatory variable is a simple linear regression; a model with two or more explanatory variables is a multiple linear regression. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Linear regressionA 5-minute orientation
- Skim a recent paper: Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effectsInfluential work since 2022
- Pick up a classic book: Applied Regression ModelingThe most-reprinted book on the subject
- Read the landmark paper: Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear EstimationCited 6,754 times
- Go sideways: Ordinary least squaresA closely related topic
- Go sideways: Least squaresA closely related topic
📖 Definitions
Noun A linear equation for deriving a single predicted value from one or more known explanatory values.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
Generalized Inverses, Ridge Regression, Biased Linear Estimation, and Nonlinear Estimation
Detection of Influential Observation in Linear Regression
Linear regression
Advanced Statistics: Linear Regression, Part II: Multiple Linear Regression
A New Regression Model: Modal Linear Regression
Linear Regression Analysis
Multiple linear regression
Simple linear regression
🆕 Influential since 2022
Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects
Fine-scale modeling of the urban heat island: A comparison of multiple linear regression and random forest approaches
Research on linear regression algorithm
Synthetic Control as Online Linear Regression
Toward practical privacy-preserving linear regression
📚 Books worth reading
Applied Regression ModelingIain Pardoe · 2006
Applied Linear RegressionSanford Weisberg · 2005
Generalized, linear, and mixed modelsCharles E. McCulloch, Shayle R. Searle · 2003
Design of ExperimentsMax Morris · 2009- 📕Methods and Applications of Linear ModelsRonald R. Hocking · 2003
Applied linear models with SASDaniel Zelterman · 2010
Applied linear statistical modelsJohn Neter · 1974
Generalized additive modelsTrevor Hastie · 1985
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Ordinary least squares In statistics, ordinary least squares (OLS) is a type of linear least squares method for choosing the unknown parameters in a linear regression model by…
- Least squares In numerical linear algebra and regression analysis, least squares is a method to determine the best-fit model by minimizing the sum of the squared residuals—th…
- Linear least squares Linear least squares (LLS) is the least squares approximation of linear functions to data. It is a set of formulations for solving statistical problems…
- Simple linear regression In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample…
- Regression analysis In statistical modeling, regression analysis is a statistical method for estimating the relationship between a dependent variable (often called the outcome…
- Lasso (statistics) In statistics and machine learning, lasso (least absolute shrinkage and selection operator; also Lasso, LASSO or L1 regularization) is a regression analysis…
- Instrumental variables In statistics, econometrics, epidemiology and related disciplines, the quasi-experimental method of instrumental variables (IV) is used to estimate causal…
- Residual sum of squares In statistics, the residual sum of squares (RSS), also known as the sum of squared residuals (SSR) or the sum of squared estimate of errors (SSE), is the…