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You can use the Sound object to modify a soundtrack movie that is loaded into the base movie or to modify a sound in the document Library. After you create an instance of the Sound object, you attach the sound that you want to modify. You can create the instance on a keyframe, within a movie clip, or within a button. To create an instance of the Sound object: 1. Select the object or keyframe where you want to create the instance. 2. Click Actions Variables and then double-click set variable. 3. Enter a name in the Variable field. This is the name you will use to refer to the Sound object. Enter a logical name such as mySound or globalSound. 4. Place your cursor in the Value field, choose Objects Movie Sound, and then double-click new Sound. That s all you need to do to create an instance of the Sound object. If you re going to use buttons to load movie soundtracks into a document, use the first keyframe in the movie to create the Sound object instance. Then you can create the ActionScript to load the sound and attach the sound when a button is clicked. If you re creating a movie clip that you ll use to control the soundtrack in a movie, create the Sound object instance on the first frame of the movie clip.



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FIGURE 4-4

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where n is the sample size It is this connection with the variance that wreaks havoc when using any method based on means A classic illustration of why is based on a particular mixed (or contaminated) normal distribution where with probability 9 an observation is sampled from a standard normal distribution and otherwise sampling is from a normal distribution having a standard deviation of 10 Figure 31 shows the standard and mixed normal distributions The mixed normal is said to have thick or heavy tails because its tails lie above the normal curve, which implies that unusually small or large values, called outliers, are more common when sampling from the mixed normal versus the normal As is evident, the two distributions are very similar in a certain sense, but there is a crucial difference: The standard normal has variance 1, but the mixed normal has variance 109.

1. Open the le Lab3A.pptx from the Labs folder (from the CD accompanying the book) and save it as MyLab3A.pptx. 2. Display the Slide Master. To do so, choose View Slide Master.

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This illustrates the well-known result that an arbitrarily small change in any distribution, including normal distributions as a special case, can cause the variance to become arbitrarily large That is, 2 is extremely sensitive to the tails of a distribution One implication is that arbitrarily small departures from normality can result in low power (relative to other methods we might use) when comparing means To begin to appreciate that alternative estimators can make a practical difference in applied work, consider the median versus the mean Figure 32 shows a plot of 5,000 medians and means, each based on 20 observations randomly sampled from the mixed normal shown in Figure 31 Note that the medians are more tightly clustered around zero, the value being estimated, than are the means That is, the median has a much smaller standard error than the mean, which can translate into more power.

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However, if observations are sampled from a standard normal distribution instead, the plot of the medians versus the means now appears as shown in Figure 33 That is, using medians can result in low power relative to using the mean (as well as other estimators described later in this chapter) To provide an explicit illustration regarding the effect of nonnormality on power when using means, suppose that 25 observations are randomly sampled from each of two normal distributions both having variance 1, the rst having mean 0 and the second having mean 1 Applying Student s T test with.

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