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3. Test hypotheses and construct con dence intervals on the ratio of the variances or standard deviations of two normal distributions 4. Test hypotheses and construct con dence intervals on the difference in two population proportions 5. Use the P-value approach for making decisions in hypotheses tests 6. Compute power, type II error probability, and make sample size decisions for two-sample tests on means, variances, and proportions 7. Explain and use the relationship between con dence intervals and hypothesis tests CD MATERIAL 8. Use the Fisher-Irwin test to compare two population proportions when the normal approximation to the binomial distribution does not apply Answers for many odd numbered exercises are at the end of the book. Answers to exercises whose numbers are surrounded by a box can be accessed in the e-Text by clicking on the box. Complete worked solutions to certain exercises are also available in the e-Text. These are indicated in the Answers to Selected Exercises section by a box around the exercise number. Exercises are also available for some of the text sections that appear on CD only. These exercises may be found within the e-Text immediately following the section they accompany.

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This This This This is is is is the the the the Verbose Trace = 4 Info Trace = 3 Warning Trace = 2 Error Trace = 1

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The previous chapter presented hypothesis tests and con dence intervals for a single population parameter (the mean , the variance 2, or a proportion p). This chapter extends those results to the case of two independent populations. 2 The general situation is shown in Fig. 10-1. Population 1 has mean 1 and variance 1, 2 while population 2 has mean 2 and variance 2 . Inferences will be based on two random samples of sizes n1 and n2, respectively. That is, X11, X12, p , X1n1 is a random sample of n1 observations from population 1, and X21, X22, p , X2n2 is a random sample of n2 observations from population 2. Most of the practical applications of the procedures in this chapter arise in the context of simple comparative experiments in which the objective is to study the difference in the parameters of the two populations.

10-2

If the value of the trace setting is changed in the Web.config file, the Switch class rereads the Web.config file and uses the new settings.

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In this section we consider statistical inferences on the difference in means 1 2 of two normal distributions, where the variances 2 and 2 are known. The assumptions for this sec1 2 tion are summarized as follows.

If X1 and X2 are independent random variables with probability density functions fX1(x1) and fX2 (x2), respectively, the probability density function of Y X1 X2 is fY 1 y2 fX1 1 y x2 f X2 1x2 dx (S5-5)

The Debug Monitor Utility (DbMon.exe) is a tool that displays the messages that have been written to the OutputDebugString function in the Kernel32 API. This tool can display Trace and Debug messages that have been written using the DefaultTraceListener. The Debug Monitor Utility is provided with the Windows Platform SDK. Figure 14.2 shows the Trace output when running the previous Trace example code. The benefit of this tool is that a program that contains many trace messages can be run outside of Visual Studio .NET and the Debug Monitor Utility will listen for the trace messages and display them. This does not require a recompilation of the program, and the program works normally as the messages are displayed.

The probability density function of Y in Equation S5-5 is referred to as the convolution of the probability density functions for X1 and X2. This concept is commonly used for transformations (such as Fourier transformations) in mathematics. This integral may be evaluated numerically to obtain the probability density function of Y, even for complex probability density functions for X1 and X2. A similar result can be obtained for discrete random variables with the integral replaced with a sum. In some problems involving transformations, we need to nd the probability distribution of the random variable Y h(X) when X is a continuous random variable, but the transformation is not one to one. The following result is helpful.

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