You can transform your data to fit a normal distribution in order to satisfy the assumptions for the analysis.
 No
transformation: Do not use a transformation if your data already follow a normal distribution. To determine the distribution of your data, or whether a transformation will be effective if your data are nonnormal, use Individual
Distribution Identification.
 BoxCox power
transformation (W = Y^λ): Use the BoxCox transformation if your nonnormal data are all positive (> 0) and you want to obtain estimates of withinsubgroup (potential) capability as well as overall capability. The BoxCox transformation is a simple, easytounderstand transformation.
Select the lambda (λ) value that Minitab uses to transform the data.
 Use optimal
λ: Use the optimal lambda, which should produce the best fitting transformation. Minitab rounds the optimal lambda to 0.5 or the nearest integer.
Note
To use an exact value instead of a rounded value for optimal λ, choose and deselect Use rounded values
for BoxCox transformations when possible.
 λ = 0 (ln): Use the natural log of your data.
 λ = 0.5 (square
root): Use the square root of your data.
 Other (enter a value
between 5 and 5): Use a specified value for lambda. Other common transformations are square (λ = 2), inverse square root (λ = −0.5), and inverse (λ = −1). In most cases, you should not use a value outside the range of −2 and 2.
 Johnson transformation
(for overall analysis only): Use the Johnson transformation if your nonnormal data contain negative values (or 0) or if the BoxCox transformation is not effective. The Johnson transformation function is more complicated than BoxCox, but is very powerful for finding an appropriate transformation.
 PValue to select best
fit

Enter a value between 0 and 1. The value that you enter defines the significance level for a normality test of the data before and after the transformation. A higher value makes the criteria for normality more rigorous. A lower value makes the criteria for normality less stringent.