Use the regression equation to describe the relationship between the response and the terms in the model. The regression equation is an algebraic representation of the regression line. The regression equation for the linear model takes the following form: Y= b0 + b1x1. In the regression equation, Y is the response variable, b0 is the constant or intercept, b1 is the estimated coefficient for the linear term (also known as the slope of the line), and x1 is the value of the term.
The regression equation with more than one term takes the following form:
y = b0 + b1X1 + b2X2 + ... + bkXk
If the model contains both continuous and categorical variables, the regression equation table can display an equation for each combination of levels for the categorical variables. To use these equations for prediction, you must choose the correct equation, based on the values of the categorical variables, and then enter the values of the continuous variables.
If the regression equation table does not specify coded or uncoded units, it is in uncoded units.
For a regression equation in coded units, interpret the coefficients using the coded values instead of the natural units. For more information, go to All statistics for Coefficients table and click "Coded coefficients".
If the regression equation table does not specify coded or uncoded units, it is in uncoded units.
For a regression equation that is in uncoded units, interpret the coefficients using the natural units of each variable. For more information, see "Regression equation".
You can view the coded coefficients in the Coefficients table.