Multiple Linear Regression
Multiple linear regression is a generalization of linear regression by considering more than one independent variable, and a specific case of general linear models formed by restricting the number of dependent variables to one. The basic model for linear regression is
In the formula above we consider n observations of one dependent variable and p independent variables. Thus, Yi is the ith observation of the dependent variable, Xij is ith observation of the jth independent variable, j = 1, 2, ..., p. The values βj represent parameters to be estimated, and εi is the ith independent identically distributed normal error.
Read more about this topic: General Linear Model
Famous quotes containing the word multiple:
“Combining paid employment with marriage and motherhood creates safeguards for emotional well-being. Nothing is certain in life, but generally the chances of happiness are greater if one has multiple areas of interest and involvement. To juggle is to diminish the risk of depression, anxiety, and unhappiness.”
—Faye J. Crosby (20th century)