A line in a two dimensional or two-variable space is defined by the equation Y=a+b*X; in full text: the Y variable can be expressed in terms of a constant (a) and a slope (b) times the X variable. The constant is also referred to as the intercept, and the slope as the regression coefficient or B coefficient. In general then, multiple regression procedures will estimate alinear equation of the form:
Y = a + b1*X1 + b2*X2 + ... +bp*Xp
Note that in this equation, the regression coefficients (or B coefficients) represent the independent contributions of each independent variable to the prediction of the dependent variable. However, their values may not be comparable between variables because they depend on the units of measurement or ranges of the respective variables. Some software products will produce both the raw regression coefficients (B coefficients) and the
Beta coefficients (note that the Beta coefficients are comparable across variables).
See also, the
Multiple Regression chapter.
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