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Listing 3 9. MyDocument.m #import "MyDocument.h" @implementation MyDocument @synthesize textView; @synthesize string; - (id)init { self = [super init]; if (self) { } return self; } - (NSString *)windowNibName { return @"MyDocument"; } - (void)windowControllerDidLoadNib:(NSWindowController *) aController { [super windowControllerDidLoadNib:aController]; if (self.string != nil) { [[textView textStorage] setAttributedString:self.string]; } } - (NSData *)dataOfType:(NSString *)typeName error:(NSError **)outError { NSData *data; self.string = textView.textStorage; data = [NSArchiver archivedDataWithRootObject:self.string]; return data; } - (BOOL)readFromData:(NSData *)data ofType:(NSString *)typeName error:(NSError **)outError { NSAttributedString *tempString = [NSUnarchiver unarchiveObjectWithData: data]; self.string = tempString; return YES; } @end

One common way to describe the uncertainty associated with an estimate is to give an interval within which the true value is expected to fall, along with the probability with which it is expected to fall into this interval Such estimates are called conjdence interval estimates

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Definition: An N% confidence interval for some parameter p is an interval that is expected with probability N% to contain p

You re not quite done yet. You know that you created a new outlet in MyDocument.h now you need to build the connections for this in Interface Builder. Make sure that you have saved MyDocument.h and MyDocument.m, and then double-click on MyDocument.xib. There are two complementary connections you need to make here. First, right-click on File s Owner and drag a connection out to the Text View (make sure you hit this and not the Scroll View). When you drop the connection, choose textView from the HUD dropdown list. Now right-click on the Text View and drag a connection out to File s Owner. When you drop it, choose delegate. Save the NIB, return to Xcode, and Build and Run. You should now be able to create new documents, add text and drag in images, and then save them. You should also be able to reopen saved documents.

For example, if we observe r = 12 errors in a sample of n = 40 independently drawn examples, we can say with approximately 95% probability that the interval 030 f 014 contains the true error errorv(h) How can we derive confidence intervals for errorv(h) The answer lies in the fact that we know the Binomial probability distribution governing the estimator errors(h) The mean value of this distribution is errorV(h), and the standard deviation is given by Equation (59) Therefore, to derive a 95% confidence interval, we need only find the interval centered around the mean value errorD(h),

which is wide enough to contain 95% of the total probability under this distribution This provides an interval surrounding errorv(h) into which errors(h) must fall 95% of the time Equivalently, it provides the size of the interval surrounding errordh) into which errorv(h) must fall 95% of the time For a given value of N how can we find the size of the interval that contains N% of the probability mass Unfortunately, for the Binomial distribution this calculation can be quite tedious Fortunately, however, an easily calculated and very good approximation can be found in most cases, based on the fact that for sufficiently large sample sizes the Binomial distribution can be closely approximated by the Normal distribution The Normal distribution, summarized in Table 54,is perhaps the most well-studied probability distribution in statistics As illustrated in Table 54, is a bell-shaped distribution fully specified by its it