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Box-Cox Transformation

This page defines the Box-Cox power transformation that Quantum XL uses to bring skewed positive data closer to a normal distribution before capability or control chart math runs on the transformed values.

Notation

Term Description
\(x_i\) an original (untransformed) data value; must be positive
\(y\) a transformed value
\(\lambda\) Box-Cox power parameter, limited to \([-5, 5]\)
\(g\) geometric mean of the data, \(g = \left(\prod_i x_i\right)^{1/N}\)
\(W_i(\lambda)\) scaled transform used only to select \(\lambda\)

The transformation

\[ y = \begin{cases} x^{\lambda} & \lambda \ne 0 \\[4pt] \ln x & \lambda = 0 \end{cases} \]

Quantum XL uses the simple power form (not the scaled form \((x^{\lambda} - 1)/\lambda\)), so the transformed values are directly comparable to transformed specification limits. The transformation requires every value to be positive, at least 3 non-missing values, and non-zero variation. \(\lambda\) is either supplied by the user or fitted automatically; in both cases it is limited to \([-5, 5]\).

Selecting lambda

The automatic \(\lambda\) minimizes the spread of the scaled transform

\[ W_i(\lambda) = \begin{cases} \dfrac{(x_i/g)^{\lambda} - 1}{\lambda} & \lambda \ne 0 \\[8pt] \ln(x_i/g) & \lambda = 0 \end{cases} \]

where \(g\) is the geometric mean of the data (dividing by \(g\) makes spreads comparable across \(\lambda\)). The objective depends on the data structure:

  • Subgrouped data: minimize the pooled within-subgroup standard deviation of \(W(\lambda)\).
  • Individual observations: minimize the moving-range sigma of \(W(\lambda)\).

The minimum is found with Brent's bounded minimization after a coarse scan brackets it.

Specification limits and the target

Specification limits and the target are transformed with the same \(y = x^{\lambda}\) (or \(\ln x\)) rule so capability is evaluated consistently in the transformed domain. A negative \(\lambda\) reverses order, so the transformed LSL and USL swap roles; Quantum XL performs this swap internally and reports results in the user's original orientation.

Used by

See Also

References

  1. Box, G. E. P., and Cox, D. R. (1964). An analysis of transformations. Journal of the Royal Statistical Society: Series B (Methodological), 26(2), 211-252.
  2. Brent, R. P. (1973). Algorithms for Minimization Without Derivatives. Prentice-Hall.
  3. Sakia, R. M. (1992). The Box-Cox transformation technique: A review. Journal of the Royal Statistical Society: Series D (The Statistician), 41(2), 169-178.
  4. Rodriguez, R. N. (1992). Recent developments in process capability analysis. Journal of Quality Technology, 24(4), 176-187.