Bland-Altman parcels were also used to investigate a possible link between the differences between the measurements and the actual value (i.e. proportional distortion). The existence of proportional distortion indicates that the methods do not uniformly correspond to the range of measures (i.e., the limits of compliance depend on the actual measure). To formally assess this relationship, the difference between methods should be reduced to the average of the two methods. If a relationship between differences and actual value has been identified (i.e. a significant slope of the regression line), 95% regression-based agreements should be indicated.  Bland JM, DG Altman. (1999) Measurement agreement in comparative study of methods. Statistical methods in medical research 8, 135-160. Note that the x values for the diagram in Figure 2 of Figure 2 of Bland-Altman-Plot are between 30 and 80, and therefore in the V2:Y3 range of Figure 1 (which is a repetition of Figure 4 of the Bland-Altman diagram), we give the finish points for the three horizontal lines (for the average and the lower and upper and upper limits) in Figure 2. To compare the Bland Altman measurement systems, the differences between the different measurements of the two different measurement systems are calculated and the average and the standard deviation are calculated. The 95% of “agreement limits” are calculated as the average of the two values minus and plus 1.96 standard deviation. This 95 per cent agreement limit should include the difference between the two measurement systems for 95 per cent of future measurement pairs.
We can present these limits for the difference compared to the average plot: a Bland-Altman plot (differential diagram) in analytical chemistry or biomedicine is a method of data representation used for the analysis of agreement between two different assays. It is identical to a tube of average difference Tukey, the name under which it is known in other areas, but it was popularized in the medical statistics of J. Martin Bland and Douglas G. Altman.   Bland-Altman plots are widely used to assess the agreement between two instruments or two measurement techniques. Bland-Altman plots identify systematic differences between measures (i.e. fixed pre-stress) or potential outliers. The average difference is the estimated distortion, and the SD of the differences measures random fluctuations around this average.
If the average value of the difference based on a 1-sample-t test deviates significantly from 0, this means the presence of a solid distortion. If there is a consistent distortion, it can be adjusted by subtracting the average difference from the new method. It is customary to calculate compliance limits of 95% for each comparison (average difference ± 1.96 standard deviation of the difference), which tells us how much the measurements were more likely in two methods for most people. If the differences in the average± 1.96 SD are not clinically important, the two methods can be interchangeable. The 95% agreement limits can be unreliable estimates of population parameters, especially for small sampling sizes, so it is important to calculate confidence intervals for 95% compliance limits when comparing methods or evaluating repeatability.