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Table 12B POSTSECONDARY EDUCATION ATTENDANCE
"... In PAGE 35: ...3 This analysis includes dropouts. 1 Table12 A displays marriage, childbearing behavior, and PSE attendance of all students. Students who are married, have children, or expect these events by age 21 are less likely to attend PSE than those who do not.... In PAGE 35: ... This compares to 76 percent of students who did not expect to be married by age 21. Table12 B shows the same relationships for the low income, high test score sample. Table 12A POSTSECONDARY EDUCATION ATTENDANCE BY MARRIAGE AND CHILDBEARING BEHAVIOR 1 Students Who Were Students Who Were Not % % Who go to % % Who go to PSE PSE Married by second follow-up 1.... ..."
Table 12B POSTSECONDARY EDUCATION ATTENDANCE
"... In PAGE 35: ...3 This analysis includes dropouts. 1 Table12 A displays marriage, childbearing behavior, and PSE attendance of all students. Students who are married, have children, or expect these events by age 21 are less likely to attend PSE than those who do not.... In PAGE 35: ... This compares to 76 percent of students who did not expect to be married by age 21. Table12 B shows the same relationships for the low income, high test score sample. Table 12A POSTSECONDARY EDUCATION ATTENDANCE BY MARRIAGE AND CHILDBEARING BEHAVIOR 1 Students Who Were Students Who Were Not % % Who go to % % Who go to PSE PSE Married by second follow-up 1.... ..."
Table 2 lists the factors and asociated information (if known) such as units, ranges, type of parameter (continuous or categorical) and restrictions on seting the parameter. Figure 2 defines the factors on the LAS drawing. As discussed earlier each configuration wil be evaluated over 10 pre-defined Mach numbers: 0.7, 0.9, 0.95, 1.05, 1.1, 1.3, 1.46, 1.96, 2.74, and 4.0. An integrated drag coeficient was developed as a weighted sum of coeficients from diferent Mach numbers based on dynamic presure and time in order to calculate a single drag value for each experimental run.
in LAS
"... In PAGE 4: ... Table2 . Factors/Control Variables ... ..."
Table 2 shows the factor analysis of the seventeen variables which New Zealand online buyers used to measure the quality of websites most recently visited. This factor analysis extracted four factors from the seventeen variables. Each factor was defined by at least three scale items. The result is consistent with the findings of Wolfinbarger and Gilly [2002]. The reason was probably that fourteen out of seventeen items borrowed from their scale measured the
2005
"... In PAGE 8: ... This factor explains 15% of the total variation, and indicates the importance of this factor in the study of online shopping behavior. Table2 Rotated Factor Matrices of Perceived Factors Affecting Online Purchase by Online Buyers Variables Factors 1 2 3 4 1 The website provides in-depth information. 0.... ..."
Cited by 1
Table 2: Breast cancer p53 study: Biological annotation for the latent factors defined by the model analysis. The p53 status column simply refers to the direct association between p53 mutational status and the posterior mean of the factor scores in a univariate model. See Figures 8 and 9 for some detailed investigation of the variation in factor levels across samples, and of their visual associations with the two binary ER (positive/negative) and p53 (mutant/wildtype) phenotypes.
2005
"... In PAGE 30: ... All factors have top genes that are known to be associated with cell cycle and oncogenic activity. Table2 presents a summary of some of the top gene-factor pairings, with comments on their association with the two binary responses ER positive/negative and p53 mutant/wildtype. Relevant aspects of factor variation over samples and are displayed in Figures 8 and 9.... In PAGE 47: ...ER Cell Development and Apoptosis Immunoregulation Cell Development and Apoptosis Figure 8: Breast cancer p53 study. Several of the estimated latent factors that show direct association with p53 Status ( Table2 ). Note also the interesting evidence of association between ER and p53 in some of these factors.... ..."
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Table 2. The subscripts 1 and 0 are used to indicate results from TimberWolfSC apos;s annealing schedule and the new annealing schedule, respectively. The quantities T~ (the speedup factor) and Wl are defined as 1
"... In PAGE 3: ...) I Name 1 Size lTF 1 Wire length . T1 I w 1 wo I w, 1463 sda2 469 209 apos;2 752 449 1 5655 800 4880 sda harris Table2 : Comparison with Ti~nberWolfSC apos;s annealing schedule. Paper 22.... ..."
Table 1: Characteristics of the test matrices. n is the number of unknowns. nnz(A) and nnz(LU) defines the number of nonzeros in A and LU and #flops the floating point operations to obtain the factorization.
"... In PAGE 5: ... Nested dissection was used to order these matrices [7]. Table1 summarizes some global, characteristic data of the test matrices. The matrices are sorted in increasing order of #flops/nnz(LU), the ratio of floating point operations (#flops) to the number of nonzeros in the factored matrix (nnz(LU)).... ..."
Table 1. Define the following matrix:
in Noise Robust Oversampled Linear Phase Perfect Reconstruction Filter Bank With A Lattice Structure
"... In PAGE 1: ... From this This research was partially supported by the Ministry of Education, Science, Sports and Culture, Grant-in-Aid for JSPS Fellows, 1210303, 2000. Table1 . A summary of previous works for a lattice structure of an N-channel LPPRFB with decimation factor M: E(z) and R(z) de- note the polyphase matrices of the analysis bank and the synthesis bank, respectively.... ..."
Table 3. Recurring factors between two design approaches We used the design model of Romme (2003) as a basis for explaining how the factors relate for designing tailorable technologies. The Romme model acts as a way to deductively package the set of factors for describing the design of tailorable technologies. The model comprises a set of factors that collectively portray a unique configuration that defines the purpose of a system, describes its outcomes, and focuses on the development of design theory (Romme, 2003). The factors represent proposed, not governing principles about designing tailorable technologies. The factors are intended to control the complexity of the design process as well as create usable technology. Specifically, the factors operationalize two design environments: the reflective and the active environments. The reflective environment describes how knowledge and content are used in the service of action. The active environment employs the knowledge and content in the form of action (Romme, 2003). Table 4 defines the nine factors and their relationship to both the reflective and active environments.
"... In PAGE 9: ...Table3... ..."
Table 4. Nine factors for designing tailorable technologies
"... In PAGE 9: ... The active environment employs the knowledge and content in the form of action (Romme, 2003). Table4 defines the nine factors and their relationship to both the reflective and active environments. ... ..."
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