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After careful study of this chapter, you should be able to do the following: 1. Determine situations where nonparametric procedures are better alternatives to the t-test and ANOVA 2. Use one- and two-sample nonparametric tests 3. Use nonparametric alternatives to the single-factor ANOVA 4. Understand how nonparametric tests compare to the t-test in terms of relative ef ciency Answers for most 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.

The GradientBevelFilter class is much like the BevelFilter class, except that rather than adding areas of highlight and areas of shadow to the display object, the entire area is lled with gradations between the values you provide. The class de nes the following properties:

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alphas The alphas of the bevel speci ed as an array of values from 0.0 to 1.0 such that each element corresponds to an element in the colors array. The default is null. ratios The ratios to use when distributing the colors across the bevel speci ed as an array of values from 0 to 255. Each element of the array corresponds to an element of the colors array. Each color in the colors array blends into the next, so each color appears at 100 percent at just one point in the continuum. blurX The amount to blur the bevel in the x direction from 0 to 255. The default is 4. blurY The amount to blur the bevel in the y direction from 0 to 255. The default is 4. strength The punch strength of the bevel from 0 to 255. The default is 1. quality The number of times to run the blur on the bevel. The range is from 0 to 15. The higher the number, the smoother the blur of the bevel appears. The default is 1. type The type can be one of the constants BitmapFilterType.INNER, BitmapFilterType.OUTER, or BitmapFilterType.FULL. The default is BitmapFilterType.INNER. knockout A Boolean value indicating whether the original display object contents should be transparent. If true, that section of the object is transparent. (The background or whatever is underneath the object is visible.) The default value is false.

Most of the hypothesis-testing and con dence interval procedures discussed in previous chapters are based on the assumption that we are working with random samples from normal populations Traditionally, we have called these procedures parametric methods because they are based on a particular parametric family of distributions in this case, the normal Alternately, sometimes we say that these procedures are not distribution-free because they depend on the assumption of normality Fortunately, most of these procedures are relatively insensitive to slight departures from normality In general, the t- and F-tests and the t-con dence intervals will have actual levels of signi cance or con dence levels that differ from the nominal or advertised levels chosen by the experimenter, although the difference between the actual and advertised levels is usually fairly small when the underlying population is not too different from the normal.

public synchronized void send(GU1Message msg) m-networkStack.sendGUIMessage(msg);

The GradientBevelFilter constructor accepts between 0 to 11 parameters from the preceding list. The following code constructs a new GradientBevelFilter with the default values:

In this chapter we describe procedures called nonparametric and distribution-free methods, and we usually make no assumptions about the distribution of the underlying population other than that it is continuous These procedures have actual level of signi cance or con dence level 100(1 )% for many different types of distributions These procedures have considerable appeal One of their advantages is that the data need not be quantitative but can be categorical (such as yes or no, defective or nondefective) or rank data Another advantage is that nonparametric procedures are usually very quick and easy to perform The procedures described in this chapter are competitors of the parametric t- and F-procedures described earlier Consequently, it is important to compare the performance of both parametric and nonparametric methods under the assumptions of both normal and nonnormal populations In general, nonparametric procedures do not utilize all the information provided by the sample.

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