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
In the regression equation, the letters represent the following:
- y is the response variable
- b0 is the constant
- b1, b2, ..., bk are the coefficients
- x1, x2, ..., xk are the values of the term
If the model contains both continuous and categorical variables, the regression equation table can display an equation for each level of the categorical variable. 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.