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Minitab can use either the (0, 1) or (−1, 0, +1) coding scheme to include categorical variables in the model. The (0, 1) scheme is the default for regression and Cox regression analyses while the (−1, 0, +1) scheme is the default for ANOVA and DOE. The choice between these two schemes does not change the statistical significance of the categorical variables. However, the coding scheme does change the coefficients and how to interpret them.
Verify the coding scheme that is displayed to ensure that you performed the intended analysis. Interpret the coefficients for the categorical variables as follows:
If you chose to standardize the continuous predictors in your model, Minitab provides details about the method in the Continuous predictor standardization table.
Usually, you use standardization to center variables, to scale variables, or both. When you center variables, you reduce multicollinearity caused by polynomial terms and interaction terms, which improves the precision of the coefficient estimates. In most cases, when you scale variables, Minitab converts the different scales of the variables to a common scale, which lets you compare the size of the coefficients.
When you use a Box-Cox transformation, the estimated λ (lambda) is the optimal value to produce transformed response values that are normally distributed. By default, Minitab uses the rounded lambda value.
Lambda is the exponent that Minitab uses to transform the response data. For example, if lambda = -1, then all response values (Y) are transformed as follows: −Y-1 = −1/Y. If lambda equals 0, this represents the natural log of Y rather than Y0.
The confidence intervals for λ (lambda) are ranges of values that are likely to contain the true value of λ for the entire population from which your sample was drawn.
Because samples are random, two samples from a population are unlikely to yield identical confidence intervals. However, if you take many random samples, a certain percentage of the resulting confidence intervals contain the unknown population parameter. The percentage of these confidence intervals that contain the parameter is the confidence level of the interval.
Use the confidence interval to assess the estimate of lambda for your sample.
For example, with a 95% confidence level, you can be 95% confident that the confidence interval contains the value of lambda for the population. The confidence interval helps you assess the practical significance of your results. Use your specialized knowledge to determine whether the confidence interval includes values that have practical significance for your situation. If the interval is too wide to be useful, consider increasing your sample size.
By default, Minitab rounds the optimal λ (lambda) to the nearest half because these values correspond to a more intuitive transformation. If you want to use the optimal value for the transformation, choose .
| Lambda | Transformation |
|---|---|
| -2 | −Y-2 = −1 / Y2 |
| -1 | −Y-1 = −1 / Y |
| -0.5 | −Y-0.5 = −1 / (square root of Y) |
| 0 | log (Y) |
| 0.5 | Y0.5 = square root of Y |
| 1 | Y |
| 2 | Y2 |
When you use a test data set, the table shows the percentage of the data that are in the test data set. When you use cross-validation, the table shows the number of folds. When you specify a column that specifies which observations are in the test data set or which observations are in each fold, then the table shows the title of the column.
Verify the validation method that is in the results to ensure that you performed the intended analysis.
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