The Johnson transformation optimally selects one of three families of distribution to transform the data to follow a normal distribution.

Johnson family | Transformation function | Range |
---|---|---|

S_{B} |
γ + η ln [(x – ε) / (λ + ε – x)] | η, λ > 0, –∞ < γ < ∞ , –∞ < ε < ∞, ε < x < ε + λ |

S_{L} |
γ + η ln (x – ε) | η > 0, –∞ < γ < ∞, –∞ < ε < ∞, ε < x |

S_{U} |
γ + η Sinh^{–1} [(x – ε) / λ] , where
Sinh |
η, λ > 0, –∞ < γ < ∞, –∞ < ε < ∞, –∞ < x < ∞ |

The algorithm uses the following procedure:

- Considers almost all potential transformation functions from the Johnson system.
- Estimates the parameters in the function using the method described in Chou, et al.
^{1} - Transforms the data using the transformation function.
- Calculates Anderson-Darling statistics and the corresponding p-value for the transformed data.
- Selects the transformation function that has the largest p-value that is greater than the p-value criterion (default is 0.10) that you specify in the Transform dialog box. Otherwise, no transformation is appropriate.

Term | Description |
---|---|

S_{B} | The Johnson family distribution with the variable bounded (B) |

S_{L} | The Johnson family distribution with the variable lognormal (L) |

S_{U} | The Johnson family distribution with the variable unbounded (U) |

For more information on the Johnson transformation, see Chou, et al.^{1} Minitab replaces the Shapiro-Wilks normality test used in that text with the Anderson-Darling test.

For information on the probability plot, percentiles, and their confidence intervals, go to Methods and formulas for distributions in Individual Distribution Identification.