# Strategi binari 30 minitab

Provides a cost-effective methodology for the evaluation of factors whose sum total volume or quantity cannot change. For example, if you wish to add more fruit filling to an 8-ounce fruit bar, another ingredient must be reduced.

Such adjustments are common in packaged food and chemical formulations. If you have discrete numeric data from which you can obtain every equally spaced value and you have measured at least strategi binari 30 minitab possible values, you can evaluate these data as if they strategi binari 30 minitab continuous. Summary Provides a cost-effective methodology for the evaluation of factors whose sum total volume or quantity cannot change. Which ingredients have the largest effects on the process output which ingredients are the key ingredients?

Do important interactions exist between ingredients? Which process inputs have the largest effects on the process output which inputs are the key inputs? Do important interactions exist between process inputs? How much variation in the process output can be explained by varying the ingredients and process inputs?

What is the optimal combination of ingredients? What are the optimal settings of the process inputs? When to Use Purpose Mid-project If you believe the desired characteristics of the strategi binari 30 minitab are a function of only the ingredients, use a pure mixture DOE to evaluate which ingredients have the largest influence on the characteristics, build a predictive model using the key ingredients, and find the optimal quantities of the ingredients.

Mid-project If you believe the desired characteristics of the mixture are a function of both the ingredients and the process, use a mixed model DOE some factors are ingredients, some are process inputs to evaluate which ingredients and process inputs have the largest influence on the characteristics. Then, build a predictive model using the key ingredients and key process inputs and find the optimal quantities of the ingredients along with the optimal settings of the process inputs. Data Continuous Y, continuous X's for a pure mixture design.

Verify the measurement systems for the Y data and the ingredients are adequate. Develop a data collection strategy who should collect the data, as well as where and when; how many data values are needed; the preciseness of strategi binari 30 minitab data; how to record the data, and so on.

Run the experiment and reduce to a final model by eliminating interaction terms such as binary blending terms with high p-values typically greater than 0.

All linear terms must stay in the model, **strategi binari 30 minitab** they are part of the formulation; removing a linear term would mean you remove an ingredient strategi binari 30 minitab the mixture. Use either the response optimizer or mixture contour plots to determine optimal settings of the ingredients. Generate the prediction equation. Guidelines First, you should strategi binari 30 minitab a sound data collection strategy to ensure you are basing your conclusions on truly representative data.

Whenever possible, you should do the runs in the experiment in random order to prevent confusing a factor effect with the effect of an untested factor sometimes called a lurking variable. The residuals of the final model must be reasonably normal and with reasonably equal variance. The residuals are usually analyzed by a histogram, normal probability plot, residuals versus fits, and residuals versus order, which can be run at one time using the Four in one option. Minitab allows the use of mixed strategi binari 30 minitab designs mixture-process experiments in which you use a combination of traditional and mixture DOE approaches.

For example, a 1-pound cake recipe has six ingredients as part of its mixture component and has the process variables temperature and time as part of its standard DOE. The process variables X's can be discrete such as fan on or off.

Minitab also allows a mixture DOE analysis in which the relative proportions of the components as well as the total volume of strategi binari 30 minitab mixture are analyzed in the same design mixture-amounts experiments.

For example, use the cake example from above, evaluate the results when you bake 1-pound, 2-pound, and 3-pound cakes. Check for possible outliers in the unusual observations table Session window output.

Do not extrapolate beyond your inference space. You can then analyze this newly defined, custom design in the usual manner. By using this site you agree to the use of cookies for analytics and personalized content. If you believe the desired characteristics of the mixture are a function of only the ingredients, use a pure mixture Strategi binari 30 minitab to evaluate which ingredients have the largest influence on the characteristics, build a predictive model using the key ingredients, and find the optimal quantities of the ingredients.

If you believe the desired characteristics of the mixture are a function of both the ingredients and the process, use a mixed model DOE some factors are ingredients, some are process inputs to evaluate which ingredients and process inputs have the largest influence on the characteristics.