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For example, earlier we found that the estimated probability that a 72-year-old patient has the disease is 61%, and the estimated probability that the 72-year-old patient does not have the disease is 39% Thus, the odds of a 72-year-old patient having the disease is odds = 061/039 = 156 We also found that the estimated probabilities of a 50-year-old patient having or not having the disease are 26% and 74%, respectively, providing odds for the 50-year-old patient to be odds = 026/074 = 035 Note that when the event is more likely than not to occur, odds > 1; when the event is less likely than not to occur, odds < 1; and when the event is just as likely as not to occur, odds = 1.



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Note also that the concept of odds differs from the concept of probability, since probability ranges from zero to 1, and odds can range from zero to in nity Odds indicate how much more likely it is that an event occurred compared to its not occurring.

= Pr W (ij ) Y(j ) , W (j 1) , . . . , W (j m) = W (ij ) ,





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In binary logistic regression with a dichotomous predictor, the odds that the response variable occurred (y = 1) for records with x = 1 can be denoted as e 0 + 1 /(1 + e 0 + 1 ) (1) = = e 0 + 1 1 (1) 1/(1 + e 0 + 1 ) Correspondingly, the odds that the response variable occurred for records with x = 0 can be denoted as e 0 /(1 + e 0 ) (0) = = e 0 1 (0) 1/(1 + e 0 ) We also need to discuss the odds ratio (OR), de ned as the odds that the response variable occurred for records with x = 1 divided by the odds that the response variable occurred for records with x = 0.

Handbook of Wireless Networks and Mobile Computing, Edited by Ivan Stojmenovic Copyright 2002 John Wiley & Sons, Inc. ISBNs: 0-471-41902-8 (Paper); 0-471-22456-1 (Electronic)

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That is, (1) [1 (1)] OR = (0) [1 (0)] e 0 + 1 = e 0 1 =e (43) The odds ratio has come into widespread use in the research community because of this simply expressed relationship between the odds ratio and the slope coef cient..

for pharmacologic interventions that have been shown to reduce subsequent morbidity to be effective (Krantz & Lundgren, 1998, p. 211). It is imperative that future research in the elds of coronary heart disease and hypertension continues to focus on the roles of stress and psychosocial parameters as risk factors for these diseases. In addition, future research studies must advance our understanding of the mechanisms by which biobehavioral factors impact CHD and hypertension. A greater understanding of these physiological and behavioral mechanisms will enable health care professionals to better prevent and manage cardiac disorders, improving the quality of life for millions of people worldwide.

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The four methods of model selection we have discussed are essentially optimization algorithms over a large sample space. Because of that, there is no guarantee that the globally optimal model will be found; that is, there is no guarantee that these variable 2 selection algorithms will uncover the model with the lowest s, the highest Radj , and so on [2,6]. The only way to ensure that the absolute best model has been found is simply to perform all the possible regressions. Unfortunately, in data mining applications, there are usually so many candidate predictor variables available that this method is simply not practicable. Not counting the null model y = 0 + , there are 2 p 1 possible models to be built using p predictors.

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