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.^{ [1] }^{ [2] }^{ [3] } It can contain indicator variables (ones and zeros) that indicate group membership in an ANOVA, or it can contain values of continuous variables.

- Definition
- Size
- Examples
- Arithmetic mean
- Simple linear regression
- Multiple regression
- One-way ANOVA (cell means model)
- One-way ANOVA (offset from reference group)
- See also
- References
- Further reading

The design matrix contains data on the independent variables (also called explanatory variables) in statistical models which attempt to explain observed data on a response variable (often called a dependent variable) in terms of the explanatory variables. The theory relating to such models makes substantial use of matrix manipulations involving the design matrix: see for example linear regression. A notable feature of the concept of a design matrix is that it is able to represent a number of different experimental designs and statistical models, e.g., ANOVA, ANCOVA, and linear regression.^{[ citation needed ]}

The design matrix is defined to be a matrix such that (the *j*^{th} column of the *i*^{th} row of ) represents the value of the *j*^{th} variable associated with the *i*^{th} object.

A regression model which is a linear combination of the explanatory variables may therefore be represented via matrix multiplication as

where *X* is the design matrix, is a vector of the model's coefficients (one for each variable), and *y* is the vector of predicted outputs for each object.

The matrix of data has dimension *n*-by-*p*, where *n* is the number of samples observed, and *p* is the number of variables (features) measured in all samples.^{ [4] }^{ [5] }

In this representation different rows typically represent different repetitions of an experiment, while columns represent different types of data (say, the results from particular probes). For example, suppose an experiment is run where 10 people are pulled off the street and asked four questions. The data matrix *M* would be a 10×4 matrix (meaning 10 rows and 4 columns). The datum in row *i* and column *j* of this matrix would be the answer of the *i*^{th} person to the *j*^{th} question.

The design matrix for an arithmetic mean is a column vector of ones.

This section gives an example of simple linear regression—that is, regression with only a single explanatory variable—with seven observations. The seven data points are {*y*_{i}, *x*_{i}}, for *i* = 1, 2, …, 7. The simple linear regression model is

where is the *y*-intercept and is the slope of the regression line. This model can be represented in matrix form as

where the first column of 1s in the design matrix allows estimation of the *y*-intercept while the second column contains the *x*-values associated with the corresponding *y*-values.

This section contains an example of multiple regression with two covariates (explanatory variables): *w* and *x*. Again suppose that the data consist of seven observations, and that for each observed value to be predicted (), values *w*_{i} and *x*_{i} of the two covariates are also observed. The model to be considered is

This model can be written in matrix terms as

Here the 7×3 matrix on the right side is the design matrix.

This section contains an example with a one-way analysis of variance (ANOVA) with three groups and seven observations. The given data set has the first three observations belonging to the first group, the following two observations belonging to the second group and the final two observations belonging to the third group. If the model to be fit is just the mean of each group, then the model is

which can be written

In this model represents the mean of the th group.

The ANOVA model could be equivalently written as each group parameter being an offset from some overall reference. Typically this reference point is taken to be one of the groups under consideration. This makes sense in the context of comparing multiple treatment groups to a control group and the control group is considered the "reference". In this example, group 1 was chosen to be the reference group. As such the model to be fit is

with the constraint that is zero.

In this model is the mean of the reference group and is the difference from group to the reference group. is not included in the matrix because its difference from the reference group (itself) is necessarily zero.

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- the fibers as the discontinuous or dispersed phase,
- the matrix as the continuous phase, and
- the fine interphase region, also known as the interface.

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In statistics and in machine learning, a **linear predictor function** is a linear function of a set of coefficients and explanatory variables, whose value is used to predict the outcome of a dependent variable. This sort of function usually comes in linear regression, where the coefficients are called regression coefficients. However, they also occur in various types of linear classifiers, as well as in various other models, such as principal component analysis and factor analysis. In many of these models, the coefficients are referred to as "weights".

In electromagnetism, a branch of fundamental physics, the **matrix representations of the Maxwell's equations** are a formulation of Maxwell's equations using matrices, complex numbers, and vector calculus. These representations are for a homogeneous medium, an approximation in an inhomogeneous medium. A matrix representation for an inhomogeneous medium was presented using a pair of matrix equations. A single equation using 4 × 4 matrices is necessary and sufficient for any homogeneous medium. For an inhomogeneous medium it necessarily requires 8 × 8 matrices.

In statistics, **linear regression** is a linear approach to modelling the relationship between a scalar response and one or more explanatory variables. The case of one explanatory variable is called *simple linear regression*; for more than one, the process is called **multiple linear regression**. This term is distinct from multivariate linear regression, where multiple correlated dependent variables are predicted, rather than a single scalar variable.

- ↑ Everitt, B. S. (2002).
*Cambridge Dictionary of Statistics*(2nd ed.). Cambridge, UK: Cambridge University Press. ISBN 0-521-81099-X. - ↑ Box, G. E. P.; Tiao, G. C. (1992) [1973].
*Bayesian Inference in Statistical Analysis*. New York: John Wiley and Sons. ISBN 0-471-57428-7. (Section 8.1.1) - ↑ Timm, Neil H. (2007).
*Applied Multivariate Analysis*. Springer Science & Business Media. p. 107. - ↑ Johnson, Richard A; Wichern, Dean W (2001).
*Applied Multivariate Statistical Analysis*. Pearson. pp. 111–112. ISBN 0131877151. - ↑ "Basic Concepts for Multivariate Statistics p.2" (PDF). SAS Institute.

- Verbeek, Albert (1984). "The Geometry of Model Selection in Regression". In Dijkstra, Theo K. (ed.).
*Misspecification Analysis*. New York: Springer. pp. 20–36. ISBN 0-387-13893-5.

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