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Although we will not prove it here, it can be shown that 2 gives an unbiased estimate of d; that is E[C ] = d What is the probability distribution governing the random variable 2 From earlier sections, we know that for large nl and n2 (eg, both 2 30), both errors, ( h l ) and error&( h z ) follow distributions that are approximately Normal Because the difference of two Normal distributions is also a Normal distribution, 2 will also

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follow a distribution that is approximately Normal, with mean d It can also be shown that the variance of this distribution is the sum of the variances of errors, ( h l ) and errors2(h2)Using Equation (59) to obtain the approximate variance of each of these distributions, we have

The code in bold is what you added to the stubs provided by Xcode. So, start by cutting and pasting that code into the corresponding files in your new iPhone application. Make the MainViewController.h file look like Listing 4 3 (you need to add the code in bold):

(512)

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Now that we have determined the probability distribution that governs the estimator 2, it is straightforward to derive confidence intervals that characterize the likely error in employing 2 to estimate d For a random variable 2 obeying a Normal distribution with mean d and variance a2, the N% confidence interval estimate for d is 2 f z ~ a Using the approximate variance a given above, this ; approximate N% confidence interval estimate for d is

Listing 4 3. MainViewController.h #import "FlipsideViewController.h" @interface MainViewController : UIViewController <FlipsideViewControllerDelegate> { IBOutlet NSTextField *destinationTextField; IBOutlet NSTextField *sourceTextField; } - (IBAction)showInfo; - (IBAction)sayHello:(id)sender;

+ errors2(h2)(1n2 errors2(h2))

.

(513)

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I m going to confess at this point that there is a big deliberate mistake in what you have just done. Can you spot it (Hint: Objects in the iPhone user interface may not have exactly the same names as similar objects in the Mac OS X interface.) Also, make a mental note here that when you get to Interface Builder you are going to need to create outlets for destinationTextField and sourceTextField and the sayHello: action. Before we get to that, let s look at the code for MainViewController.m. You ll see that there is a lot of stub code there, commented out but ready for you to elaborate should you need to. There are also a few methods there for handling the flip functionality. For this application you won t need to change anything that s there, but just copy the implementation of sayHello: and paste it into this class file. Add the code that s in bold in Listing 4 4; I put it immediately after the closing braces of the initWithNibName method.

where zN is the same constant described in Table 51 The above expression gives the general two-sided confidence interval for estimating the difference between errors of two hypotheses In some situations we might be interested in one-sided bounds--either bounding the largest possible difference in errors or the smallest, with some confidence level One-sided confidence intervals can be obtained by modifying the above expression as described in Section 536 Although the above analysis considers the case in which hl and h2 are tested on independent data samples, it is often acceptable to use the confidence interval seen in Equation (513) in the setting where h 1 and h2 are tested on a single sample S (where S is still independent of hl and h2) In this later case, we redefine 2 as

The variance in this new 2 will usually be smaller than the variance given by 1 Equation (512), when we set S and S2 to S This is because using a single sample S eliminates the variance due to random differences in the compositions of S1 and S2 In this case, the confidence interval given by Equation (513) will generally be an overly conservative, but still correct, interval

Listing 4 4. MainViewController.m [ ] - (IBAction)sayHello:(id)sender { [destinationTextField setStringValue:[sourceTextField stringValue]]; } [ ]

In some cases we are interested in the probability that some specific conjecture is true, rather than in confidence intervals for some parameter Suppose, for example, that we are interested in the question "what is the probability that errorv(h1) > errorv(h2) ' Following the setting in the previous section, suppose we measure the sample errors for hl and h2 using two independent samples S1 and S2 of size 100 and find that errors, ( h l ) = 30 and errors2(h2)= -20, hence the observed difference is 2 = l o Of course, due to random variation in the data sample,

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