# Select the graphs to display for Analyze Binary Response for Factorial Design

Stat > DOE > Factorial > Analyze Factorial Design > Graphs Stat > DOE > Factorial > Analyze Binary Response > Graphs

Specify the options for the graphs.

## Effects Plots

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 Binary Response for 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 Binary Response for Factorial Design.

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.
The normal probability plot displays negative effects on the left side of the graph and positive effects on the 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.
The half normal plot displays the absolute value of all effects, positive and negative. Instead of putting negative effects to the left and positive effects to the right, all the significant effects are on the right side, which emphasizes their relative magnitudes.

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.

## Residuals

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.
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.
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