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Most of all, she would like to thank him for his unique ability to keep her in a continuous state of ow Her daughter, Nicole, for an independence and attitude beyond her years and great enthusiasm for this and several other of her mom s projects Faculty and staff members of the College of Engineering and Computer Science at California State University Fullerton, in particular, the Dean, Dr Raman Unnikrishnan, and Patti McCarthy for their encouragement and continuous support throughout the duration of this and several other projects, along with Phyllis Harn and Sandra Boulanger Finally, her co-author, Adam Kolawa, for his vision to initiate this interesting idea Adam would like to thank all Parasoft employees, who over the past 15 years helped in the development and implementation of the ideas described in this book Without their creativity, dedication, and hard work this book would not be possible.

1 Deletion of Problem Variables In practice, the most widely used method for dealing with quasi-complete separation is simply to delete from the model any variables whose coef cients did not converge I do not recommend this method If a variable has quasi-complete separation with the outcome variable, it is reasonable to suppose that the variable has a strong (albeit nonin nite) effect on the outcome variable Deleting variables with strong effects will certainly obscure the effects of those variables and is also likely to bias the coef cients for other variables in the model 1062 Combining Categories As noted earlier, the most common cause of quasi-complete separation is a dummy predictor variable such that for one level of the variable, either every observation has the event or no observation has the event.

Task Group s Mesh (MES )

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For those cases in which the problem variable is one of a set of variables representing a single categorical variable, the problem can often be solved easily by combining categories For example, suppose that marital status has ve categories: never married, currently married, divorced, separated, and widowed This variable could be represented by four dummy variables, with currently married as the reference category Suppose further that the sample contains 50 persons who are divorced but only 10 who are separated If the outcome variable is 1 for employed and 0 for unemployed, it s quite plausible that all 10 of the separated persons would be employed, leading to quasi-complete separation A natural and simple solution is to combine the divorced and separated categories, turning two dummy variables into a single dummy variable.

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In particular, he would like to acknowledge Marek Kucharski, Marek Pilch, Wayne Ariola, and Erika Delgado as well as the writers, especially Cynthia Dunlop for her help in writing and editing the book, along with Marvin Gapultos and Tes Kurtz..

Similar problems often arise when a quantitative variable such as age is divided into a set of categories with dummy variables for all but one of the categories Although this can be a useful device for representing nonlinear effects, it can easily lead to quasi-complete separation if the number of categories is large and.

Another use for heterodyning is in frequency synthesis. This can be represented in a manner similar to Fig. 7.2, but the RF and LO are xed or synthesized frequencies, and the object is to combine them to produce a new synthesized frequency at the IF.4 Here we have control over the signals existing in the RF, rather than being subject to whatever is picked up by an antenna, so no RF lter is required. We also have control of signal levels. Now the spurious responses of interest are IF signals, produced by the intended, actual, RF, that are passed by the IF lter. We must prevent these undesired signals in the IF, and the acceptable level of such signals in the output is often much lower than for the receiver. Heterodyning in exciters, which provide signals for transmitters, is similar to that in synthesizers. In upconverters, the mixer port that is labeled IF may be used as the input port because its designated frequency range is lower than the port labeled RF. This is generally acceptable, but we may need a different spur level chart (Fig. 7.3) for this usage. Regardless of its label on the physical device, we will still call the input port the RF port in our discussions.

Figure 14.3 indicates that there are clusters: Small-volume customers may be, to a high degree, unpro table. (Note that this does not automatically suggest abandoning all of them, because some may be the seedlings that will grow into forests, becoming the large, profitable customers of the future.) For many of the larger customers, pro ts grow exponentially. For a sizable group of large customers, however, pro ts are minimal or, worse yet, nonexistent and negative. There can be medium-sized customers that, as a collective group, bring in a large share of the total pro t. The shape of any organization s customer base s pro tability pro le curve will be unique. Knowing the types of customers that cluster in the various pro t/loss zones of Figure 14.3 can be valuable in determining what actions to take. PM goes even further by bundling these pro t margin and cost measurement tools with employee scorecard tools to assure that any decisions are aligned with

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2448 0.6355 0.005 0.9509 0.0021 370.3 0.4262 0.0201 0.3829 0.0474 73.7 0.5158 0.1144 0.7634 0.0082 3621 0.6266 0.0094 0.7062 0.0034

For q = 6, the minimum fault current that can be detected is:

250 4

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