Equivalence Testing in .NET Integrated 2d Data Matrix barcode in .NET Equivalence Testing Visual Studio .NET barcodes

Equivalence Testing using barcode encoder for none control to generate, create none image in none applications.print pdf-417 Equivalence Test versus Diff none for none erence Test Suppose that you have developed a new process, with reduced costs, for manufacturing an electric motor. The motor is currently in production and its characteristics are well known. In this situation you don t have a process that s supposed to deliver an improved result.

Instead you have to determine if the old and new processes provide results that are the same within some range of indifference. If you can show that they do, you can say that the processes are equivalent. In that case, you can implement the new cost-saving process, con dent that you are producing acceptable products.

Other examples of situations where you might be interested in demonstrating equivalence include the following: After a value engineering project on a medical device you feel that you have made signi cant cost reductions without reducing its quality and reliability. You have to show that the performance of the value-engineered device is equivalent to that of the existing product. Your supplier tells you that if they substitute a variant of an existing reagent in a diagnostic test kit, production costs will be reduced.

This may enable higher margins and perhaps increased market share. You must show that the new test is equivalent to the old test..

Visual Studio 2010 From the Library of Wow! eBook Equivalence Testing You are building a comp uter model to simulate a complex system. At what point in the model s re nement can you say that it s good enough You could compare model results with actual performance and use equivalence testing to make your case. Although equivalence testing looks at differences, it does so in a way that s different from the two-sample t-test.

In a difference test there is a hypothesis pair that is generally of the form H0: m1 m2 = d Ha: m1 m2 d In a difference test using the two-sample t-test, by setting the a risk to a small level you place the burden of proof on showing that any difference between the means is signi cant at that a level. If p < a, you reject the null hypothesis and accept the alternate. However, you fail to reject the null hypothesis if you fall short with p > a.

So the evidence has to be pretty strong to reject the null and accept the alternate hypothesis. In the typical difference test, the objective is to understand if the difference between two treatments is signi cantly different. In the equivalence test, you must show that they are not different by more than a certain amount.

Next we describe two approaches for testing equivalence: the con dence interval method and the method of two one-sided tests (TOST).. Con dence Interval Method for Showing Equivalence The con dence interval me thod for showing equivalence is intuitive and straightforward. The steps in the process are as follows: 1. Decide how large a difference, d, you can accept between means before you conclude that you do not have equivalence.

This is the most dif cult and important decision you will make in the process. It s not a statistical decision, but a decision requiring knowledge of how performance variations affect both the end user of your product and your business. As mentioned in earlier examples, it s a business decision.

This is an opportunity to apply the concept of quality loss discussed in 2. 2. Take samples from both populations.

3. Calculate the con dence interval for the difference in population means. Note that this is a two-sided con dence interval with a in each tail.

So if you want 95% con dence, use a 90% con dence interval with 5% in each tail of the distribution. You do it this way because you don t know in advance whether the difference will be biased toward + d or d. The con dence interval for the difference in means is (X1 X2 ) t1 a,n 1s 6 (m1 m2 ) 6 (X1 X2 ) + t1 a,n 1s.

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