Specify the options for Response Optimizer

Response Optimizer > Options

In This Topic

Constraints
Starting values
Confidence level for all intervals
Type of confidence interval

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.