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 Variability. 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 Variability.
###### Note

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 Variability and click "Lenth's pseudo standard error (PSE)."

- 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.

- Residuals for Plots
- Specify the type of residuals to display on the residual plots. For more information, go to Residuals in analyze variability.
- Ratio: Plot the ratio residuals.
- Ln: Plot the log residuals.
- Standardized ln: Plot the standardized log 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.
- Residuals versus fits
- Display the residuals versus the fitted values.
- Residuals versus order
- Display the residuals versus the order of the data. The observation number for each data point is shown on the x-axis.

- Three in one: Display all three 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.