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In statistics, a design matrix is a matrix of values of explanatory variables, often denoted by X, that is used in certain statistical models, e.g., the general linear model.〔 (Section 8.1.1)〕 It can contain indicator variables (ones and zeros) that indicate group membership in an ANOVA, or it can contain values of continuous variables. 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. ==Definition== In a regression model, written in matrix-vector form as : the matrix ''X'' is the design matrix, while ''y'' is the vector of observations on the dependent variable, is a vector of response coefficients (one for each explanatory variable) and is a vector containing the values of the model's error term for the various observations. In the design matrix, each column is a vector of observations on one of the explanatory variables. 抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』 ■ウィキペディアで「Design matrix」の詳細全文を読む スポンサード リンク
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