Stat > DOE > Factorial > Analyze Factorial Design > Options
Use to:
When you transform your data, Minitab transforms the response data and uses it in the analysis. Under most conditions, it is not necessary to correct for nonnormality unless the data are highly skewed. When you use Box-Cox, all response data must be greater than 0.
Note |
Minitab cannot calculate the optimal l (lambda) when you use a stepwise procedure. |
Weights: Enter a numeric column of weights to perform weighted regression. Weights must be greater than or equal to zero. The length of the weights column must match the length of the response column. Weights cannot be used with a split-plot design.
Confidence level for all intervals: Enter the confidence level. The default is 95.
Type of confidence interval: Choose the type of confidence interval: two-sided (default), lower bound, or upper bound.
Box-Cox Transformation
No transformation: Choose to use your original response data.
Optimal l (lambda): Choose to have Minitab search for an optimal value.
l = 0 (natural log): Choose to use the natural log of the data.
l = 0.5 (square root): Choose to use the square root of the data.
l : Choose to transform the data using another l value. Enter a value between -5 and 5.
Means table: Use this drop-down list to quickly display means for groups of terms. If an expected term does not appear in either list, add it to the model. You can choose one of these options:
None
Main effects
Two-way interactions
All terms in the model
Specified terms
For more information on these options, see Displaying Means for Model Terms.
If you choose Display means for specific terms, use the arrow keys to move terms from one list to the other. Select one or more terms in one of the lists, then click an arrow key. The double arrows move all the terms in one list to the other. You can also move a term by double-clicking it.
Available Terms: Shows all terms that you can display means for.
Selected Terms: Minitab displays the means for the terms shown in Selected Terms when it fits the model.