DOE (design of experiments) helps you investigate the effects of input variables (factors) on an output variable (response) at the same time. These experiments consist of a series of runs, or tests, in which purposeful changes are made to the input variables. Data are collected at each run. You use DOE to identify the process conditions and product components that affect quality, and then determine the factor settings that optimize results.

Only Minitab offers a unique, integrated approach by providing software and services that drive business excellence from anywhere with the cloud. Key statistical tests include t tests, one and two proportions, normality test, chi-square, and equivalence tests. Access new levels of productivity and collaboration, whether you are using Minitab on.

1. Pricing Details (Provided by Vendor): Minitab offers a free trial for 30 days. Paid plans with single and multiple user licenses. Details include: Single-User Annual License starting from $1,610 Volume discounts available. Pricing Resources: Be an Informed Buyer: Understanding the True Cost of Business Software.
2. Minitab 17 Free Trial Download Windows Version Of Google Earth Speaking Skills Pdf Windows Live Mail Windows 7 Dynascape Design Manual Best Chess Learning Software Wii Download Ticket Generator Winamp Download Free Windows 7 Free Obd Auto Doctor Key Simple Wallhack Cs 1.6 Vb Net Download.

Minitab offers five types of designs: screening designs, factorial designs, response surface designs, mixture designs, and Taguchi designs (also called Taguchi robust designs). The steps you follow in Minitab to create, analyze, and visualize a designed experiment are similar for all types. After you perform the experiment and enter the results, Minitab provides several analytical tools and graph tools to help you understand the results. This chapter demonstrates the typical steps to create and analyze a factorial design. You can apply these steps to any design that you create in Minitab.

Minitab DOE commands include the following features:

• Catalogs of designed experiments to help you create a design
• Automatic creation and storage of your design after you specify its properties
• Display and storage of diagnostic statistics to help you interpret the results
• Graphs to help you interpret and present the results

In this chapter, you investigate two factors that might decrease the time that is needed to prepare an order for shipment: the order-processing system and the packing procedure.

The Western center has a new order-processing system. You want to determine whether the new system decreases the time that is needed to prepare an order. The center also has two different packing procedures. You want to determine which procedure is more efficient. You decide to perform a factorial experiment to test which combination of factors enables the shortest time that is needed to prepare an order for shipment.

Interpret the results

The two-way ANOVA table includes terms for the part, the operator, and the part-operator interaction. If the p-value for the interaction is ≥ 0.05, Minitab omits the interaction from the full model because it is not significant. In this example, the p-value is 0.974, so Minitab generates a second two-way ANOVA table that omits the interaction from the final model.

Use the variance components (VarComp) to compare the variation from each source of measurement error to the total variation. In these results, the %Contribution column in the Gage R&R table shows that the variation from Part-To-Part is 92.24%. This value is much larger than Total Gage R&R, which is 7.76%. Thus, much of the variation is due to differences between parts.

Use %Study Var to compare the measurement system variation to the total variation. The Total Gage R&R equals 27.86% of the study variation. The Total Gage R&R %Contribution might be acceptable depending on the application. For more information, go to Is my measurement system acceptable?.

For this data, the number of distinct categories is 4. According to the AIAG, you need at least 5 distinct categories to have an adequate measuring system. For more information, go to Using the number of distinct categories.

Minitab 17 Trial Version

The graphs also provide the following information about the measurement system:

Minitab 17 Trial

• In the Components of Variation graph, the %Contribution from Part-To-Part is larger than that of Total Gage R&R. Thus, much of the variation is due to differences between parts.
• The R Chart by Operator shows that Operator B measures parts inconsistently.
• In the Xbar Chart by Operator, most of the points are outside the control limits. Thus, much of the variation is due to differences between parts.
• The By Part graph shows that the differences between parts are large.
• In the By Operator graph, the differences between operators are smaller than the differences between parts, but are significant (p-value = 0.00). Operator C's measurements are slightly lower than the measurements of the other operators.
• In the Operator* Part Interaction graph, the lines are approximately parallel and the p-value for the Operator*Part interaction found in the table is 0.974. These results indicate that no significant interaction between each Part and Operator exists.