1. Statistical Reference Guide
  2. Measurement systems analysis (MSA)
  3. Trueness

Estimating the linearity of a measurement procedure (EP06-Ed2)Determine whether a measurement system or procedure provides measured quantity values (within a measuring interval) that are directly proportional.

  1. Select a cell in the dataset.
  2. On the Analyse-it ribbon tab, in the Statistical Analyses group, click Linearity.
  3. In the Y drop-down list, select the measured variable.
  4. In the By drop-down list, select the level variable, and then:
    • If the values are identifiers, select the Identifier, and then in the Assigned values grid, under the Value column alongside each level, type the value.
    • If the values are dilutions made by diluting a high pool or mixing high and low pools, select the Relationship, and then select Mixture, Dilution, or Addition based on how the levels were prepared. In the Assigned values grid, under the Value column for the first and/or last level, type the value (intermediate values are automatically calculated using relative values).
    • If the values are known/expected/assigned values, select the Known values.
    Note: Computation of linearity only requires the relationship between the levels, you do not need to enter the assigned values if they are unknown. For CLSI EP06-Ed2 Design A, use Dilution, and for Design B, use Mixture.
  5. Optional: To compare the nonlinearity bias against performance requirements:
    • If the allowable nonlinearity bias is a constant or proportional value across the measuring interval, select Across measuring interval, and then in the Absolute edit box, type the bias in measurement units, and/or in the Relative edit box, type the bias as a percentage (suffix with % symbol).
      Note: The allowable bias is the greater of the absolute bias and the relative bias for each level. Therefore, with a absolute bias of 5mg/dL and a relative bias of 10%, the allowable bias will be set at 5mg/dL for all values 0 mg/dL up to 50mg/dL and then at 10% of assigned value for values above 50mg/dL.
    • If the allowable nonlinearity bias varies for each level, select Each level and then in the Allowable nonlinearity grid, under the Absolute / Relative column alongside each level, type the bias in measurement units, or the bias as a percentage (suffix with % symbol).
  6. On the Fit Model panel, in the Fit drop-down list, select Linear.
  7. In the X drop-down list, select Expected values and in the Y down list, select Mean.
  8. In the Weights drop down list, select
    Option Description
    None Fit an ordinary regression. Use when measurement procedure exhibits constant SD over the measuring interval.
    Var(Y level) Fit a weighted regression with weight based on the variance at each level. Recommended when number of replicates per level is 4 or more.
    Var(Y level pooled) Fit a weighted regression with weight based on the pooled variance over a subinterval of levels. Recommended when number of replicates per level is 2 or 3.
    VarFn(Y) Fit a weighted regression with weight based on the variance function at the mean of each level. Recommended when the precision can be modelled by a variance function.
  9. If the levels are made by a dilution of a high level, select Force through zero check box. Otherwise, if the levels are produced by mixing a high and low level, clear the Force though origin check box.
  10. Click Calculate.
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From the Statistical Reference Guide for Analyse-it 6.24.0: https://analyse-it.com/docs/user-guide/measurement-systems-analysis/trueness/estimating-linearity2

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