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Table 3 Log-likelihood for submodels testing the gender-related association between the 93T G:D9N haplotype and quantitative traits in FCHL pedigrees

in
by Pierre N. M. Demacker B, Louis M. Havekes E, Dorret I. Boomsma C, Anton F. H. Stalenhoef B, Rune R. Frants A, Johannes J. P. Kastelein D
"... In PAGE 4: ... Therefore further submodels were tested to find out whether the effect of the 93T G:D9N haplo- type was gender dependent. Table3 shows the log- likelihoods of the full model, one mean model that... ..."

Table 2. Significant Gender-related Mean Differences for candidates

in A Study of Prospective Ophthalmology Residents ’ Career Perceptions
by Ca Gunderson, Department Of Ophthalmology, Visual Sciences
"... In PAGE 3: ...14) they expected greater levels of professional security (mean = 3.15), although lower income Table2 . Critical Factor Results (Mean of responses by candidates compared to those given by practicing physicians) Critical Career Factors 0 1 2 3 4 5 6 7 8 9 10 Autonomy Patient Time Continuity Routine/Diversity Family Time Expertise Income Innovation Intellectual Physician Interact Manual Activities Pressure Responsibility Schedule Security End Results Status applicants ... In PAGE 4: ...29). Critical Factors were then analyzed concerning different career perceptions based upon applicant gender (See Table2 ). Female residency candidates indicated two factors that were significantly different from their male counterparts: time spent directly caring for patients (p = 0.... ..."

Table 1: Sample Size amp; Gender Relative to Population

in Assessing the need and potential of community networking for developing countries: A case study from India
by Christopher Blattman, Robert Jensen, Raul Roman 2002
"... In PAGE 24: ...23 Table1 0: What is your main source(s) of information for ______? 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Weather New products (i.e.... ..."
Cited by 3

Table 2. Probabil ity of a Trait Being Gender - Related (Deaux, 1984)

in Gender differences in the perception and use of E-mail: An extension to the technology acceptance model
by Detmar W. Straub, David Gefen, David Gefen 1997
"... In PAGE 8: ...Table2... ..."
Cited by 60

Table 1: Gender related behavior patterns in online learning, 1999-2001

in Dominant or different? Gender issues in computer supported learning
by Cathy Gunn 2003
Cited by 11

Table 2. Unadjusted and Adjusted Association of Heart Rate Variability Variables With All-Cause Mortality, Cardiac Mortality, Sudden Cardiac Mortality, Sudden Cardiac Autopsy-Verified Mortality, Sudden Cardiac Mortality for Both Genders, Nonsudden Cardiac Mortality and Nonsudden Cardiac Mortality With Cerebrovascular Mortality Unadjusted Association Association Adjusted for All Risk Variables Relative

in unknown title
by unknown authors
"... In PAGE 4: ...mong men (RR 5.9, 95% CI, 2.8 to 12.5, p , 0.001). Results were similar among the subjects with autopsy- documented sudden cardiac death ( Table2 ). Short-term exponent also predicted nonsudden cardiac mortality but not cerebrovascular mortality (Table 2).... In PAGE 5: ... Multivariate predictors of mortality. Table2 shows the multivariate relative risks for HR variability measures ad- justed for other risk variables, such as age, gender, heart failure, angina pectoris, functional class, previous myocar- dial infarction, cardiac medication and ventricular prema- ture beats. Although the short-term exponent had weak associations to various clinical parameters (Table 3), it remained as a strong independent predictor of sudden cardiac death after adjustment for other variables in multi- variate analysis.... ..."

Table 1: The absolute difference in the SV performance [%] between male and female speakers for factory noise scenario

in Text-independent speaker verification for real fast-varying noisy environments
by Todor Ganchev, Ilyas Potamitis, Nikos Fakotakis, George Kokkinakis 2004
"... In PAGE 26: ...26 LIST OF TABLES Table1 : The absolute difference in the SV performance [%] between male and female speakers for factory noise scenario Table 2: The absolute difference in the SV performance [%] between male and female speakers for passing-by aircraft noise scenario Table 3: The averaged by noise type gender-related difference [%] ... ..."
Cited by 2

Table 2: The absolute difference in the SV performance [%] between male and female speakers for passing-by aircraft noise scenario

in Text-independent speaker verification for real fast-varying noisy environments
by Todor Ganchev, Ilyas Potamitis, Nikos Fakotakis, George Kokkinakis 2004
"... In PAGE 26: ...26 LIST OF TABLES Table 1: The absolute difference in the SV performance [%] between male and female speakers for factory noise scenario Table2 : The absolute difference in the SV performance [%] between male and female speakers for passing-by aircraft noise scenario Table 3: The averaged by noise type gender-related difference [%] ... ..."
Cited by 2

Table 1 depicts, in rank order, the associations that form the basis of conversational implicature around John and licence within our example. Associations of the same rank are on the same line. The ranks were determined by our expectation of highly weighted associations of the candidate terms in the utterance set. The table constitutes the solution against which the mining techniques can be measured.

in Discovery of Tacit Knowledge and Topical Ebbs and Flows within the Utterances of Online Community
by Robert Mcarthur, Peter Bruza
"... In PAGE 6: ... Table1 : apos;Perfect apos; vectors It is interesting to note, in passing, that the highest associations for John are relatively independent of context and more permanent: last name is Smith , gender is male ( he and guy ), and that he work for Microsoft . This could be seen as a global concept or generalized implicature.... ..."

Table 2: Frequency summaries of the IOIBD data.

in Summary
by Jun Yan, Cyrus P. Tamboli 2007
"... In PAGE 10: ... For IBD and its two main types, CD and UC, a mark \p quot; indicates that a variable is relevant. Table2 presents frequency summaries for several aspects of the IOIBD data, such as the composition of gender, relation, and some multichotomous variables. Due to missing values, the total number of observations for each variable is not necessarily equal to the total number of subjects in the relevant domain.... ..."
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