Example of orthogonal regressionmain topic interpreting results session command see also
Example of orthogonal regressionA medical equipment company wants to determine if their new blood pressure monitor is equivalent to a similar model on the market. They obtain systolic blood pressure readings on a random sample of 60 people using the two instruments. Based on a previously conducted study, the company knows that the error variance ratio is 0.90.
1 Open the worksheet BLOODPRESSURE.MTW.
2 Choose Stat > Regression > Orthogonal Regression.
3 In Response (Y), enter New.
4 In Predictor (X), enter Current.
5 In Error variance ratio (Y/X), enter 0.90.
6 Click OK.
Session window output
Orthogonal Regression Analysis: New versus Current
Error Variance Ratio (New/Current): 0.9
Regression Equation New = 0.644 + 0.995 Current
Coefficients
Predictor Coef SE Coef Z P Approx 95% CI Constant 0.64441 1.74470 0.3694 0.712 (-2.77513, 4.06395) Current 0.99542 0.01415 70.3461 0.000 ( 0.96769, 1.02315)
Error Variances
Variable Variance New 1.07856 Current 1.19840 |
Graph window output
0 is contained in the confidence interval for the intercept (-2.77513, 4.06395) and 1 is contained in the confidence interval for the slope (0.96769, 1.02315). Therefore, no evidence exists that the two instruments measure different things.