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Two different etching solutions have been compared, using two random samples of 10 wafers for each solution The observed etch rates are as follows (in mils per minute): Solution 1 99 106 94 103 93 100 96 103 102 101 Solution 2 102 100 106 102 107 107 104 104 105 103.

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Caching improved performance significantly over the last test where the StringBuilder was implemented. This required only a single line of code and resulted in a substantial gain. The test was done using three browser connections, and no errors were reported when running the test. Notice that the request count went from 12 to 2,204 to 15,837. This represents an increase of over seven times the previous test. It s worth noting that if the code were not changed to use the StringBuilder, the change would have been from 12 requests to 15,837 requests. This would represent an increase of over 1,300 times.

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(a) Do the data support the claim that the mean etch rate is the same for both solutions In reaching your conclusions, use 005 and assume that both population variances are equal (b) Calculate a P-value for the test in part (a) (c) Find a 95% con dence interval on the difference in mean etch rates (d) Construct normal probability plots for the two samples Do these plots provide support for the assumptions of normality and equal variances Write a practical interpretation for these plots 10-22 Two suppliers manufacture a plastic gear used in a laser printer The impact strength of these gears measured in foot-pounds is an important characteristic A random sample of 10 gears from supplier 1 results in x1 290 and s1 12, while another random sample of 16 gears from the second supplier results in x2 321 and s2 22.

(a) Is there evidence to support the claim that supplier 2 provides gears with higher mean impact strength Use 005, and assume that both populations are normally distributed but the variances are not equal (b) What is the P-value for this test (c) Do the data support the claim that the mean impact strength of gears from supplier 2 is at least 25 foot-pounds higher than that of supplier 1 Make the same assumptions as in part (a) 10-23 Reconsider the situation in Exercise 10-22, part (a) Construct a con dence interval estimate for the difference in mean impact strength, and explain how this interval could be used to answer the question posed regarding supplierto-supplier differences 10-24 A photoconductor lm is manufactured at a nominal thickness of 25 mils.

In the previous example, caching was implemented with a single line of code. In some cases, this type of caching is wasteful. For example, if a large amount of data is being sent to the browser and that same data is being sent to the browser from a different page, multiple copies of the same data could be held by the Web server. Often, part of the Web page needs to be dynamic, whereas another part of the page is cached. Object caching involves writing the code to cache objects rather than the entire page. The Cache object is used to add items into the cache. The following code shows how the BuildString method s result can be cached:

The product engineer wishes to increase the mean speed of the lm, and believes that this can be achieved by reducing the thickness of the lm to 20 mils Eight samples of each lm thickness are manufactured in a pilot production process, and the lm speed (in microjoules per square inch) is measured For the 25-mil lm the sample data result is x1 115 and s1 011, while for the 20-mil lm, the data yield x2 106 and s2 009 Note that an increase in lm speed would lower the value of the observation in microjoules per square inch (a) Do the data support the claim that reducing the lm thickness increases the mean speed of the lm Use 010 and assume that the two population variances are equal and the underlying population of lm speed is normally distributed.

(b) What is the P-value for this test (c) Find a 95% con dence interval on the difference in the two means 10-25 The melting points of two alloys used in formulating solder were investigated by melting 21 samples of each material The sample mean and standard deviation for alloy 1 was x1 420 F and s1 4 F, while for alloy 2 they were.

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