The orthogonal regression equation is an algebraic representation of the regression line and is used to describe the relationship between the response and predictor variables. The orthogonal regression equation takes the form of:

Response = intercept + slope (predictor)

or y = b0 + b1x

Where:

·    Response (y) represents the true unknown response value.

·    b0 is the value of y where the regression line intercepts (meets) the Y-axis.

·    Predictor (x) represents the true unknown predictor value.

·    b1 represent the slope or the estimated change in y for each unit change in x.

For the glucose data, the response variable is New and the predictor is Standard. The regression equation is estimated to be:

New = -0.632 + 1.007 Standard

The interpretation of the regression equation follows:

·    The slope (b1 = 1.007) is the change in New when Standard increases by 1. That is, when Standard increases by one, New increases by 1.007.

·   The intercept (b0 = -0.632) is the predicted value of New when the predictor Standard is zero. That is, when the predictor is zero, New is - 0.632.