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Explain clearly how the slope coef cient 1 and its estimate b1 may be interpreted in logistic regression Provide at least two examples, using both a categorical and a continuous predictor 45 What are odds What is the difference between odds and probability 46 What is the de nition of the odds ratio What is the relationship between the odds ratio and the slope coef cient 1 For what quantity is the odds ratio sometimes used as an estimate 47 Describe how we determine the statistical signi cance of the odds ratio using a con dence interval 48 If the difference between a particular indicator variable and the reference category is not signi cant, what should the analyst consider doing 49 Discuss the role of statistical inference with respect to the huge sample sizes prevalent in data mining 410.

Discuss the assumption that the odds ratio is constant across the range of the predictor, with respect to various types of relationships between the predictor and the response Provide modeling options for when this assumption may not be re ected in the data 411 Discuss the use of predictors that turn out to be nonsigni cant in estimating response When might this be appropriate, if at all Why would this not be appropriate in general 412 For data that are missing one or more indicator variable values, explain why it would not be appropriate simply to ignore these missing variables when making an estimation Provide options for the data analyst in this case..

Introducing Java Java Pluses and Minuses The Java virtual machine The Just-In-Time compiler Understanding the Java Security Model Java Development Tools Incorporating Java Applets Defining Options (Parameters) Testing Your Applet Introducing Server-Side Java From Here 495 496 496 497 497 498 500 500 501 501 501.

// Determine bits to get to index // Bits are written to overt2Stream... bitCnt += index2bits(index, 13-t); // Remove card from hand[][]... removeCard(index, 13-t, hand[p]); } //for p } //for t // Read scoring line... fscanf(overt2Stream, %s %s %s %s %s , &tmp[0], &tmp[1], &tmp[2], &tmp[3], // Read a NEWLINE to check for EOF... fscanf(overt2Stream, \n ); dealCnt += 3; } //while // Write bitFile to covert2... bitFile2covertFile(); return SUCCESS; } //war_creation_decode();

The introduction of VoIP in enterprises not only leads to convergence of multiple disparate networks in one physical infrastructure running only one (ie, IP) protocol, it also opens up the network for delivering several new and emerging productivity-enhancing IP-based applications and services to the employees and customers of the enterprises These new services include IP-based fax and conferencing services, uni ed messaging, nd-me/follow-me services, Web-based call/contact centers, e-commerce and customer-care services, support of virtual or remote or tele-workers, and so on Although the operational and infrastructure cost savings are the prime motivations for incorporating VoIP services in enterprises, there are other factors that contribute equally to the decision Some of these are (a) use of a uniform (ie.

tional psychometric testing before it can be used to address other investigators research questions Summary and Recommendations The SPSQ and CSPCI are in the early stages of development and need to be used in more studies and tested on a more diverse range of populations before conclusions can be made about their psychometric integrity We also note some overlap between the questionnaires (ie, two subscales of the CSPCI seem to measure concepts similar to those measured by two of the SPSQ subscales) Perhaps a combination of SPSQ and CSPCI items and scales would result in a psychometrically stronger measure of self-presentational concerns than either instrument alone Social Physique Anxiety The Social Physique Anxiety Scale (SPAS; Hart et al, 1989) measures people s dispositional tendencies to become anxious when others observe or evaluate their physique In their original paper, Hart et al proposed a 12item, unidimensional scale.

Some researchers subsequently proposed a two-factor model (eg, McAuley & Burman, 1993), but this model was largely an artifact of negatively worded, inappropriate scale items More recent studies support a unidimensional 9-item (Martin, Rejeski, Leary, McAuley, & Bane, 1997) or 7-item model in both adult and adolescent English-speaking (Motl & Conroy, 2000b; A L Smith, 2004) and non-English-speaking populations using translated versions of the SPAS (Isogai et al, 2001; Lindwall, 2004) Yet, although the 7-item model seems to provide the closest fit to the data, there is not yet consensus regarding which 7 items best fit the data, as different investigators have provided support for different 7-item models Researchers have also begun to look at the sex invariance of the SPAS factor structure and sex-differences in SPAS scores using latent means rather than observed means.