Specify the options for the graphs.

Minitab provides three graphs that help you identify the terms that influence the response: a Pareto chart, a normal plot, and a half-normal plot. These graphs allow you to compare the relative magnitude of the effects and evaluate their statistical significance.

The threshold for statistical significance depends on the significance level (denoted by α or alpha). Unless you use a stepwise selection method, the significance level is 1 minus the confidence level for the analysis. For more information on how to change the confidence level, go to Specify the options for Analyze Factorial Design. If you use backwards selection or stepwise selection, the significance level is the significance level where Minitab removes a term from the model, known as Alpha to remove. If you use forward selection, the significance level is the significance level where Minitab adds a term to the model, known as Alpha to enter. For more information on the choices for the stepwise methods, go to Perform stepwise regression for Analyze Factorial Design.
###### Note

- For a 2-level design, if the number of terms in the model equals the number of runs, the standardized effects cannot be calculated. Minitab shows the unstandardized effects and uses Lenth's method to draw a reference line for statistical significance. For more information on Lenth's method, go to Methods and formulas for the effects plots in Analyze Factorial Design and click "Lenth's pseudo standard error (PSE)."
- For a general full factorial design, if the number of terms in the model equals the number of runs, the standardized effects cannot be calculated. Minitab does not produce a Pareto chart in this case.

- Pareto
- Select to determine the magnitude and the importance of an effect. The chart displays the absolute value of the effects and draws a reference line on the chart. Any effect that extends beyond this reference line is statistically significant.
- Normal
- Select to compare the magnitude and statistical significance of main and interaction effects from a 2-level factorial design. The fitted line indicates where you would expect the points to fall if the effects were zero. Significant effects have a label and fall toward the left or right side of the graph.
- Half Normal
- Select or to compare the magnitude and statistical significance of main and interaction effects from a 2-level factorial design. The fitted line indicates where you would expect the points to fall if the effects were zero. Significant effects have a label and fall toward the right side of the graph.

For 2-level factorial and Plackett-Burman designs, select Display only model terms to display only the terms that are in the model or select Display all terms to display all of the terms in the graphs.

For split-plot designs, you can choose to:

- Display all terms, subplot effects only
- Display all terms, subplot and whole-plot effects
- Display only model terms, subplot effects only
- Display only model terms, subplot and whole-plot effects

- Residuals for Plots
- Specify the type of residuals to display on the residual plots. For more information, go to Which types of residuals are included in Minitab?.
- Regular: Plot the regular raw residuals.
- Standardized: Plot the standardized residuals.
- Deleted: Plot the Studentized deleted residuals.

- Residual Plots
- Use residual plots to examine whether your model meets the assumptions of the analysis. For more information, go to Residual plots in Minitab.
- Individual plots: Select the residual plots that you want to display.
- Histogram
- Display a histogram of the residuals.
- Normal plot
- Display a normal probability plot of the residuals.
- Residuals versus fits
- Display the residuals versus the fitted values. For a binary response, display the residuals versus the logit of fits.
- Residuals versus order
- Display the residuals versus the order of the data. The row number for each data point is shown on the x-axis.

- Four in one: Display all four residual plots together in one graph.

- Individual plots: Select the residual plots that you want to display.
- Residuals versus variables
- Enter one or more variables to plot versus the residuals. You can plot the following types of variables:
- Variables that are already in the current model, to look for curvature in the residuals.
- Important variables that are not in the current model, to determine whether they are related to the response.