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where A, B and C are 32-bit words, with C = 3,645,876,429 and a is a byte. Then what is the probability that ( A. C ) O . .=~(B)0...7 The . motivation for this choice of C can be found in Problem 22. 24. Use the results of Problenis 21 and 22, along with (3.19), to show that

Table 12-10 Test for Significance of Regression for Example 12-3 Source of Variation Regression Error or residual Total Sum of Squares 5990.7712 115.1735 6105.9447 Degrees of Freedom 2 22 24 Mean Square 2995.3856 5.2352 f0 572.17 P-value 1.08E-19

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relationship found is an appropriate model for predicting pull strength as a function of wire length and die height. Further tests of model adequacy are required before we can be comfortable using this model in practice. Most multiple regression computer programs provide the test for significance of regression in their output display. The middle portion of Table 12-4 is the Minitab output for this example. Compare Tables 12-4 and 12-10 and note their equivalence apart from rounding. The P-value is rounded to zero in the computer output. R2 and Adjusted R2 We may also use the coefficient of multiple determination R2 as a global statistic to assess the fit of the model. Computationally, R2 SSR SST 1 SSE SST (12-21)

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For the wire bond pull strength data, we find that R2 SSR SST 5990.7712 6105.9447 0.9811. Thus the model accounts for about 98% of the variability in the pull strength response (refer to the Minitab output in Table 12-4). The R2 statistic is somewhat problematic as a measure of the quality of the fit for a multiple regression model because it always increases when a variable is added to a model. To illustrate, consider the model fit to wire bond pull strength data in Example 11-8. This was a simple linear regression model with x1 wire length as the regressor. The value of R2 for this model is R2 0.9640. Therefore, adding xy die height to the model increases R2 by 0.9811 0.9640 0.0171, a very small amount. Since R2 always increases when a regressor is added, it can be difficult to judge whether the increase is telling us anything useful about the new regressor. It is particularly hard to interpret a small increase, such as observed in the pull strength data. Many regression users prefer to use an adjusted R2 statistic: SSE 1n SST 1n

1) 1)

p2 12

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Shutter Priority AE mode, or Tv (Time-variable) on the Mode dial, is the semiautomatic mode that offers you control over the shutter speed. The PowerShot G11 features electronically controlled shutter speeds from 1/4000 second to 15 seconds. In Tv mode, you control the shutter speed by turning the Control dial; the camera then automatically calculates the appropriate aperture based on the light meter reading, the metering mode, and the ISO Speed setting. The G11 then displays the aperture on the LCD monitor only when you press the Shutter Release button halfway down. If the shutter speed you have chosen allows a proper aperture to be selected, it appears in white. If your selection will cause the camera to over- or underexpose the image, the aperture value appears in orange. In either case, adjust the shutter speed slower or faster, respectively, until the aperture value turns white on the LCD, or you can set a lower or higher ISO Speed, or add or subtract Exposure Compensation. In Shutter Priority mode, you can set the white balance, My Colors, bracketing, Flash Exposure Compensation (FEC), Neutral Density, and the drive, metering, and Image Quality modes from the Function menu.

(12-22)

cO 7 ) ... cO 7 ) ...

Because SSE 1n p2 is the error or residual mean square and SST 1n 12 is a constant, R2 will adj only increase when a variable is added to the model if the new variable reduces the error mean square. Note that for the multiple regression model for the pull strength data R2 0.979 (see the adj Minitab output in Table 12-4), whereas in Example 11-8 the adjusted R2 for the one-variable model is R2 0.962. Therefore, we would conclude that adding x2 die height to the model adj does result in a meaningful reduction in unexplained variability in the response.

The adjusted R2 statistic essentially penalizes the analyst for adding terms to the model. It is an easy way to guard against overfitting, that is, including regressors that are not really useful. Consequently, it is very useful in comparing and evaluating competing regression models. We will use R2 for this when we discuss variable selection in regression in Section 12-6.3. adj

( ( X i ) 2 4...3 1

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