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The biggest challenge I nd is establishing a base exposure that still retains detail in the highlights of performers as they move in and out of the strong spotlights on the stage. To this end, I also experiment with white balance settings even though I might be getting good results with AWB. In the past, I ve gotten great results with Tungsten and custom white balances, so I alternate among the three as stage lighting changes to establish which is best. A lot of times the stage lighting is warm, and a Tungsten white balance cools things off a bit yet still produces pleasing skin tones. At concerts, I use the Auto Exposure Lock button, setting the exposure on the hottest highlight area of the performer s body. Alternately, you can use the Spot meter to determine the exposure of the face. For all indoor events on a stage, I shoot wide open at the lowest ISO that I can set and still get 1/60 second or slower at f/2.8 with my built-in IS lens. With the high ISO range of the PowerShot G11, I m excited about shooting concerts now at exposure combinations and low noise levels not previously possible.

Section 9-8 2 9-65. (a) 0 11.65, do not reject H0 (b) 0.05 P-value 0.10 2 9-67. (a) 0 25.55, reject H0 (b) P-value 0.005 2 9-69. (a) 0 10.71, do not reject H0 (b) 0.05 P-value 0.10

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where the left-hand side of the inequality is much greater than the right-hand side (by, say, a factor of 100). Let the symbol I/) denote concatenation. Trudy searches for the private key f ( z ) , where f ( x )E L ( d f , d f - 1) is of the form fo(z) /I f ~ ( z )and f o ( z ) , (of length N / 2 ) has d f / 2 coefficients of +1 and d f / 2 coefficients of -1 (and the rest, zeros) and fi(x) (of length N / 2 ) has d f / 2 ones, d f / 2 - 1 negative ones (and the rest, zeros). She wants to find f~fbx) f l (z) such that and

Statistical methods are used to help us describe and understand variability By variability, we mean that successive observations of a system or phenomenon do not produce exactly the same result We all encounter variability in our everyday lives, and statistical thinking can give us a useful way to incorporate this variability into our decision-making processes For example, consider the gasoline mileage performance of your car Do you always get exactly the same mileage performance on every tank of fuel Of course not in fact, sometimes the mileage performance varies considerably.

This observed variability in gasoline mileage depends on many factors, such as the type of driving that has occurred most recently (city versus highway), the changes in condition of the vehicle over time (which could include factors such as tire in ation, engine compression, or valve wear), the brand and/or octane number of the gasoline used, or possibly even the weather conditions that have been recently experienced These factors represent potential sources of variability in the system Statistics gives us a framework for describing this variability and for learning about which potential sources of variability are the most important or which have the greatest impact on the gasoline mileage performance We also encounter variability in dealing with engineering problems For example, suppose that an engineer is designing a nylon connector to be used in an automotive engine application.

has coefficients in {-1,0, 1). If this is the case, then g(z) (mod y) is of the correct form and therefore fo(x) 1) f l ( z ) is a candidate for the private key.

The engineer is considering establishing the design speci cation on wall thickness at 3 32 inch but is somewhat uncertain about the effect of this decision on the connector pull-off force If the pull-off force is too low, the connector may fail when it is installed in an engine Eight prototype units are produced and their pull-off forces measured, resulting in the following data (in pounds): 126, 129, 134, 123, 136, 135, 126, 131 As we anticipated, not all of the prototypes have the same pull-off force We say that there is variability in the pull-off force measurements Because the pull-off force measurements exhibit variability, we consider the pull-off force to be a random variable A convenient way to think of a random variable, say X, that represents a measurement, is by using the model X (1-1).

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