Under Predict, select the response characteristics that you want to display for factor settings using the model from your Taguchi experiment. You can also store these results in your worksheet.

- Mean (for static designs)
- Predict the average response for each combination of control factor levels in a static Taguchi design.
- Depending on your goals, the objective is to determine factor levels that either achieve a target mean, or minimize or maximize the mean. For example, you want to know how four control factors affect the mean flight distance of golf balls. Each mean provides an estimate of the flight distance for a combination of factor level settings. Because you are interested in maximizing flight distance, you want to determine the factor levels that result in the largest means.
- Slope (for dynamic designs)
- Predict the average rate of change for the response relative to the signal factor in a dynamic Taguchi design.
- Usually the goal is to choose factor levels that meet a target slope. For each factor combination in a dynamic experiment, Minitab calculates the slope of the least squares fitted line for the signal-response data passing through the reference point (if specified).
- For example, a group of scientists wants to know how five control factors affect a plant's rate of growth. There are two noise factors (temperature and humidity), and one signal factor (time). The slopes for this example provide estimates of the rate of plant growth over a range of times under the different conditions.
- Signal to Noise ratio
- Predict the signal-to-noise ratio (S/N) for each combination of control factor levels in a Taguchi design.
- Usually the goal is to choose factor levels that maximize the signal-to-noise ratio. However, you can choose from different S/N ratios, depending on the goal of your experiment, when you analyze the design.
- Standard deviation
- Predict the variability in the response due to noise.
- Usually the goal is to choose factor levels that minimize the standard deviation. Minitab calculates a separate standard deviation for each combination of control factors in the design. For example, a group of scientists wants to know how five control factors affect a plant's rate of growth. They consider two noise factors, temperature and humidity. The standard deviations for this example provide estimates of the variability in the plant growth due to temperature and humidity for each of the five control factors.
- Ln of standard deviation
- Predict the natural log of the standard deviation in the response due to noise.

Check Store predicted values in worksheet to store the predicted values in the worksheet.