Live briefing
Everything about Design matrix
Matrix of values of explanatory variables
In statistics and in particular in regression analysis, a design matrix, also known as model matrix or regressor matrix and often denoted by X, is a matrix of values of explanatory variables of a set of objects. Each row represents an individual object, with the successive columns corresponding to the variables and their specific values for that object. The design matrix is used in certain statistical models, e.g., the general linear model. It can contain indicator variables that indicate group membership in an ANOVA, or it can contain values of continuous variables. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Design matrixA 5-minute orientation
- Skim a recent paper: Active Matrix Flexible Sensory Systems: Materials, Design, Fabrication, and IntegrationInfluential work since 2022
- Pick up a classic book: Organizing for global competitivenessThe most-reprinted book on the subject
- Read the landmark paper: The design of the fibre-matrix interfacial zone in ceramic matrix compositesCited 382 times
- Go sideways: Residual sum of squaresA closely related topic
- Go sideways: General linear modelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
The design of the fibre-matrix interfacial zone in ceramic matrix composites
The systematic design of the Butler matrix
Product design knowledge management based on design structure matrix
Inhibition of Matrix Metalloproteinases: Structure Based Design
Design as integration of axiomatic design and design structure matrix
Optimal matrix-filter design
Matrix metalloprotease inhibitors: design from structure
Design with metal matrix composites
🆕 Influential since 2022
Active Matrix Flexible Sensory Systems: Materials, Design, Fabrication, and Integration
Eco-Design of Polymer Matrix Composite Parts: A Review
Towards strength-ductility enhancement of titanium matrix composites through heterogeneous grain structured Ti matrix design
Metallic Glass-Reinforced Metal Matrix Composites: Design, Interfaces and Properties
Federated Matrix Factorization: Algorithm Design and Application to Data Clustering
📚 Books worth reading
Organizing for global competitivenessRobert J. Kramer · 1993
Graphs, Matrices, and DesignsRees · 1992- 📕High temperature degradation mechanisms in polymer matrix compositesRonan A. Cunningham · 1996
- 📕A rocket engine for Mars sample return using in situ propellantsDiane L. Linne · 1997
Exercises in Computational Mathematics with MATLABTom Lyche, Jean-Louis Merrien · 2014
Using MSC/NASTRANA. O. Cifuentes · 1989- 📕Aeropropulsion '87. · 1987
Linear Algebra And MatricesHelene Shapiro · 2015
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- 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…
- General linear model The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models…
- Linear predictor function In statistics and in machine learning, a linear predictor function is a linear function (linear combination) of a set of coefficients and explanatory variables…
- Regression analysis In statistical modeling, regression analysis is a statistical method for estimating the relationship between a dependent variable (often called the outcome…
- Coefficient of determination In statistics, the coefficient of determination, denoted R2 or r2 and pronounced "R squared", is the proportion of the variation in the dependent variable…
- 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…
- 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…
- Linear regression In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory…