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Once a logistic regression formula has been generated, it can be used for prediction. In Table 4.18, two new columns are added. The logistic regression formula will calculate a probability, and from this value a classi cation can be assigned. A cutoff value, such as 0.5, can be used with a probability higher than 0.5 assigned to the 1 class and a probability less than or equal to 0.5 assigned to the 0 category. These classi cations can be used to assess the model, using a contingency table, such as the one in Fig. 4.31. The contingency table allows for the calculation of overall accuracy, error rate, speci city, sensitivity, and so on. In addition, since the model also generates a probability, a lift chart and an ROC chart can also be generated. Any models generated can be optimized by varying the independent variables to generate the simplest, most predictive model, as discussed in Section 4.3.8. The cutoff value can also be adjusted to enhance the quality of the model. References to additional methods for assessing the logistic regression model, such as the Wald test, the likelihood ratio test, and Hosmer and Lemeshow x 2 test of goodness of t are provided. Like linear regression and discriminant analysis, interaction terms (such as the product of two variables) or higher-order terms (such as a variable squared) can be computed and used with the model to enhance prediction. 5 illustrates the application of logistic regression to a number of case studies.

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The UsedRange property returns a Range object that represents the used range of the worksheet. Press Ctrl+End to activate the lower right cell of the used range. The UsedRange property can be very useful in making your functions more efficient. Consider the following Function procedure. This function accepts a range argument and returns the number of formula cells in the range.

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In the formulation above, we can see how every step must be repeated at each pose hypothesis, leading to a high computational complexity because of the very large data set x. To cope with this complexity, several approaches have been proposed in the literature (e.g., [31,39,70]). For the sake of simplicity we consider here two basic variants of the Gauss Newton algorithm: the compositional and inverse-compositional approaches, the latter providing a highspeed improvement. Following Baker and Matthews [31], the standard Lucas Kanade method may also be called forwards additive: Forwards. The direct warp function W ( x; p) is used while formulating the cost function [eq. (4.119)]. Additive. The Gauss Newton update p is added to the previous pose (step 9 of the algorithm). The rst modi cation, which we discussed in Section 2.2.1 [see eq. (2.32)] for the case of homogeneous transforms, consists of substituting the additive rule for the compositional rule: E ( p) =

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Figure 12-9 shows the division of the cumulative distribution into 10 rings for a 30-dB circular Taylor distribution where the element spacing de (r) is a linear function from 0.66 to 1.4 from the center to the edge. A ring is located in the center of each region along the radius where the maximum aperture radius is 8.5 . We space the elements evenly around each ring of radius ri : Ni = 2 ri de (ri ) (12-23)

Figure 4.36 Silhouette rendering using the dual-space method. Left: rendered surface; middle: wireframe mesh; right: model contours. Models were rendered using silhouette detection software [25]. (From [78].)

at short-term and intermediate changes in trend, gold will usually lead turns in the CRB Index by about four months on average. This being the case, the same can be said for the lead time between turns in the U.S. dollar and the CRB Index. Figure 5.16 compares all three markets from September 1988 through September 1989. The upper chart shows movement in the U.S. Dollar Index. It shows a bottom in December 1988, a bullish breakout in May 1989, and two peaks in June and September of the same year. The bottom chart compares gold and the CRB Index during the same time span. The December 1988 peak in gold (coinciding with the dollar bottom) preceded a peak in the CRB Index by a month. The setting of new lows by gold in March of 1989 provided an early warning of the impending breakdown in the CRB Index two months later. The actual breakdown in the CRB Index in May was caused primarily by the bullish breakout in the dollar that occurred during the same month.

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