Specify the options to use to analyze your factorial design.
In Weights, enter a numeric column of weights to perform weighted regression. Weighted regression is a method that can be used when the least squares assumption of constant variance in the residuals is violated (also called heteroscedasticity). With the correct weight, this procedure minimizes the sum of weighted squared residuals to produce residuals with a constant variance (also called homoscedasticity). For more information about determining the appropriate weight, go to Weighted regression.
The weights must be greater than or equal to zero. The weights column must have the same number of rows as the response column. Weights cannot be used with a split-plot design.
Enter the level of confidence for the confidence intervals for the coefficients and the fitted values.
Usually, a confidence level of 95% works well. A 95% confidence level indicates that, if you took 100 random samples from the population, the confidence intervals for approximately 95 of the samples would contain the mean response. For a given set of data, a lower confidence level produces a narrower interval, and a higher confidence level produces a wider interval.
To display the confidence intervals, you must go to the Results sub-dialog box, and from Display of Results, select Expanded tables.
Select the type of confidence interval or bound that you want to display.
You can display the means for the main effects, the main effects and two-way interactions, or all terms in the model in the output. Alternatively, you can display the means for a subset of these terms, or no terms.
If you select Specified terms, use the arrow buttons to move terms from one list to the other. Available Terms shows all the terms that you can display means for. Minitab displays the means for the terms in Selected Terms. Select one or more terms in one of the lists, then click an arrow button. The double arrows move all the terms in one list to the other. You can also move a term by double-clicking it. If a term you expected to see in the list does not appear, you need to add it to the model.