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In a designed experiment the engineer makes deliberate or purposeful changes in the controllable variables of the system or process, observes the resulting system output data, and then makes an inference or decision about which variables are responsible for the observed changes in output performance. The nylon connector example in Section 1-1 illustrates a designed experiment; that is, a deliberate change was made in the wall thickness of the connector with the objective of discovering whether or not a greater pull-off force could be obtained. Designed experiments play a very important role in engineering design and development and in the improvement of manufacturing processes. Generally, when products and processes are designed and developed with designed experiments, they enjoy better performance, higher reliability, and lower overall costs. Designed experiments also play a crucial role in reducing the lead time for engineering design and development activities. For example, consider the problem involving the choice of wall thickness for the nylon connector. This is a simple illustration of a designed experiment. The engineer chose two wall thicknesses for the connector and performed a series of tests to obtain pull-off force measurements at each wall thickness. In this simple comparative experiment, the

engineer is interested in determining if there is any difference between the 3 32- and 1 8-inch designs An approach that could be used in analyzing the data from this experiment is to compare the mean pull-off force for the 3 32-inch design to the mean pull-off force for the 1 8-inch design using statistical hypothesis testing, which is discussed in detail in s 9 and 10 Generally, a hypothesis is a statement about some aspect of the system in which we are interested For example, the engineer might want to know if the mean pull-off force of a 3 32-inch design exceeds the typical maximum load expected to be encountered in this application, say 1275 pounds Thus, we would be interested in testing the hypothesis that the mean strength exceeds 1275 pounds This is called a singlesample hypothesis testing problem.

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By adding the static keyword, you are de ning the variable wheels as part of the class instead of the instance. All Bicycle objects will now share the same wheels variable. Even if you create a eet of a thousand bikes, all will share this class variable, and any changes to it will immediately affect all the objects.

It is also an example of an analytic study 9 presents techniques for this type of problem Alternatively, the engineer might be interested in testing the hypothesis that increasing the wall thickness from 3 32- to 1 8-inch results in an increase in mean pull-off force Clearly, this is an analytic study; it is also an example of a two-sample hypothesis testing problem Two-sample hypothesis testing problems are discussed in 10 Designed experiments are a very powerful approach to studying complex systems, such as the distillation column This process has three factors, the two temperatures and the re ux rate, and we want to investigate the effect of these three factors on output acetone concentration A good experimental design for this problem must ensure that we can separate the effects of all three factors on the acetone concentration.

Now that wheels is a static variable, you use it differently. It belongs to the class Bicycle, so you use dot notation to access it from the class, not the instance.

The speci ed values of the three factors used in the experiment are called factor levels Typically, we use a small number of levels for each factor, such as two or three For the distillation column problem, suppose we use a high, and low, level (denoted +1 and 1, respectively) for each of the factors We thus would use two levels for each of the three factors A very reasonable experiment design strategy uses every possible combination of the factor levels to form a basic experiment with eight different settings for the process This type of experiment is called a factorial experiment Table 1-1 presents this experimental design Figure 1-6, on page 8, illustrates that this design forms a cube in terms of these high and low levels.

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