# Passing-Bablok regression

Passing-Bablok regression fits a line describing the relationship between two variables. It is robust, non-parametric, and is not sensitive to outliers or the distribution of errors.

Passing-Bablok regression (Passing & Bablok, 1983) finds the line of best fit using a shifted median of all possible pairwise slopes between points. It does not make assumptions of the distribution of the measurement errors, and the variance of the measurement errors need not remain constant over the measuring interval though their ratio should remain proportional to β² (the slope squared, in many cases β ≈ 1).

There is a modification to the procedure for use when the methods measure on different scales, or where the purpose is to transform results from one method to another rather than to compare two methods for equality (Passing & Bablok, 1988).

Confidence intervals for parameter estimates are based on a normal approximation or bootstrap. Confidence curves around the regression line and at specific points on the line are obtained by bootstrap.

**Related information**

- What is Analyse-it?
- What's new?
- Administrator's Guide
- User's Guide
- Statistical Reference Guide
- Distribution
- Compare groups
- Compare pairs
- Contingency tables
- Correlation and association
- Principal component analysis (PCA)
- Factor analysis (FA)
- Item reliability
- Fit model
- Method comparison
- Correlation coefficient
- Scatter plot
- Fit Y on X
- Ordinary linear regression
- Deming regression
- Passing-Bablok regression
- Fitting ordinary linear regression
- Fitting Deming regression
- Fitting Passing-Bablok regression
- Linearity
- Residual plot
- Checking the assumptions of the fit
- Average bias
- Estimating the bias between methods at a decision level
- Testing commutability of other materials
- Difference plot (Bland-Altman plot)
- Fit differences
- Plotting a difference plot and estimating the average bias
- Limits of agreement (LoA)
- Plotting the Bland-Altman limits of agreement
- Mountain plot (folded CDF plot)
- Plotting a mountain plot
- Partitioning and reducing the measuring interval
- Agreement measures for binary and semi-quantitative data
- Chance corrected agreement measures for binary and semi-quantitative data
- Agreement plot
- Estimating agreement between two binary or semi-quantitative methods
- Study design
- Study design for qualitative methods
- Measurement systems analysis (MSA)
- Reference interval
- Diagnostic performance
- Control charts
- Process capability
- Pareto analysis
- Study Designs
- Bibliography

Version 6.00

Published 27-Apr-2022