Specify the options for Response Optimizer

To perform this analysis in Minitab, go to the menu that you used to fit the model, then choose Response Optimizer > Options. For more information, go to Stored model overview.

Constraints

You can hold continuous and categorical variables at a specific value or limit the range of possible values.
Note

Covariates in a factorial design must be held at a specific value. By default, Minitab sets the value to the mean of the covariate. Covariates in a general linear model can be either unconstrained or constrained.

Variable
Displays all the variables that are included in a model. This column does not take any input.
Constraint
For each variable, select No constraints, Hold at value, or Constrain to region. Covariates in a general linear model have the additional option of Hold at mean.
Hold Value
For each variable that you specified Hold at value, enter a value at which to hold the variable. Minitab uses this value for the variable setting to calculate the fitted values.
Lower
For each variable that you specified Constrain to region, enter a minimum value. Minitab selects a value that is greater than or equal to this value.
Upper
For each variable that you specified Constrain to region, enter a maximum value. Minitab selects a value that is less than or equal to this value.

Starting values

If the algorithm produces unacceptable results, you can try to improve the results by entering a starting point for the search algorithm.
Variable
Displays all the continuous variables that are included in a fitted model. This column does not take any input.
Starting Value
Enter a value for each continuous variable. Each value must be between the minimum and maximum observed values for that variable. If you enter a constraint for a variable, the starting value must satisfy that constraint. You cannot enter a starting value when you specify a hold value for a variable.

Confidence level for all intervals

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.

Note

To display the confidence intervals, you must go to the Results sub-dialog box, and from Display of Results, select Expanded tables.

Type of confidence interval

You can select a two-sided interval or a one-sided bound. For the same confidence level, a one-sided bound is closer to the point estimate than the bounds of a two-sided interval. The upper bound does not provide a likely lower value. If you request an upper bound, then there is no lower bound. If you request a lower bound, then there is no upper bound.

For example, the predicted mean concentration of dissolved solids in water is 13.2 mg/L. The 95% confidence interval for the mean of multiple future observations is 12.8 mg/L to 13.6 mg/L. The 95% upper bound for the mean of multiple future observations is 13.5 mg/L, which is more precise because the bound is closer to the predicted mean.
Two-sided
  • Use a two-sided confidence interval to estimate both likely lower and upper values for the mean response.
  • Use a two-sided prediction interval to estimate both likely lower and upper values for a single future observation.
Lower bound
  • Use a lower confidence bound to estimate a likely lower value for the mean response.
  • Use a lower prediction bound to estimate a likely lower value for a single future observation.
Upper bound
  • Use an upper confidence bound to estimate a likely higher value for the mean response.
  • Use an upper prediction bound to estimate a likely higher value for a single future observation.
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