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Table 2 Fuzzification functions for college students College students Partition u(x)
2004
Table 3. Percentages of Persons Who Have Encouraged the Students to Attend College
"... In PAGE 7: ... 9 1%7 . 3 3 Table3 shows that 30.1% of the students reported that nobody has encouraged them to attend college.... In PAGE 7: ...1% of the students reported that nobody has encouraged them to attend college. Table3 also shows that none of the students reported that teachers have encouraged them to attend college. In most cases the students are not being encouraged to attend college.... ..."
Table 2: Typical Relationships among Feature Characteristics
1994
"... In PAGE 2: ... These characteristics have strong relationships between them. A feature metric developer may need to ascertain more detailed information re- quiring the combination of these elements, like those listed in Table2... In PAGE 3: ...Uninherited features Rede ned features Renamed features Private features Exported features Fully exported features Restricted features Attributes Routines Functions Procedures Deferred routines : : : Table 1: Feature Characteristics large Table2 would become if all possible com- binations were presented. Abstraction of commonalities can be used to il- luminate common traits and eliminate the need to separately de ne each possible combination.... In PAGE 3: ... Abstraction of commonalities can be used to il- luminate common traits and eliminate the need to separately de ne each possible combination. For example, each of the items in Table2 have at least one common property | they are all features. On closer examination, stronger rela- tionships become evident among primitives, in- cluding: inherited and uninherited features are mutually exclusive, routines are comprised only of functions and procedures, and so forth.... In PAGE 3: ... These relationships can also be thought of as metric invariants, or rules and laws that govern the collection and combination of characteris- tics, and the relationships between them. For example, from the relationships between vari- ous elements in Table2 , the following invariants, among others, can be drawn: A B A C B [ C = A C D C E D [ E = C B \ C = ? D \ E = ? A F A G F [ G = A G H G I H [ I = G F \ G = ? Combining the metric invariants just listed re- sults in an even deeper array of invariants: F = (B \ F) [ (C \ F) G = (B \ G) [ (C \ G) H = (B \ H) [ (C \ H) I = (B \ I) [ (C \ I) F = (B \ F) [ (D \ F) [ (E \ F) G = (B \ G) [ (D \ G) [ (E \ G) H = (B \ H) [ (D \ H) [ (E \ H) I = (B \ I) [ (D \ I) [ (E \ I) Metric invariants can be used to formally de- ne relationships among object-oriented design metrics. Further interrogation of the elements in Table 2 will produce a list of metric invariants exponentially larger than those just presented.... In PAGE 3: ... For example, from the relationships between vari- ous elements in Table 2, the following invariants, among others, can be drawn: A B A C B [ C = A C D C E D [ E = C B \ C = ? D \ E = ? A F A G F [ G = A G H G I H [ I = G F \ G = ? Combining the metric invariants just listed re- sults in an even deeper array of invariants: F = (B \ F) [ (C \ F) G = (B \ G) [ (C \ G) H = (B \ H) [ (C \ H) I = (B \ I) [ (C \ I) F = (B \ F) [ (D \ F) [ (E \ F) G = (B \ G) [ (D \ G) [ (E \ G) H = (B \ H) [ (D \ H) [ (E \ H) I = (B \ I) [ (D \ I) [ (E \ I) Metric invariants can be used to formally de- ne relationships among object-oriented design metrics. Further interrogation of the elements in Table2 will produce a list of metric invariants exponentially larger than those just presented. Although these examples appear relatively sim- ple, they illustrate that there are relationships among and between di erent types of metrics (both primitive and composite), and that these relationships must adhere to combination invari- ants to ensure not only correctness, but an ef- fective non-redundant design.... ..."
Cited by 2
Table 9(a). Mark-Up of Profiting and Losing Colleges in Nagaland College Students Core
"... In PAGE 16: ...8861 against 0.8611, Table9 ). Both AC and PL are statistically highly significant.... In PAGE 16: ...egative. Thus the hypothesis of mark-up pricing is maintained. This relates to our 2nd hypothesis. Table9 . Regression of Mark-Up on Average cost and Proft-Loss Dummy b see of b b see of b t(21) p-level Intercept 144.... In PAGE 18: ... Thus, the power of a private college to charge mark-up over and above its average cost draws on its cost advantages and location advantages. Table9 (b). Regression of Mark-Up on Average cost and Location Dummy b see of b b see of b t(21) p-level Intercept 11.... ..."
Table 1. Beverage consumption per day of four college students
Table 1: Comparing methods that leverage feature-class associations.
2007
Cited by 2
Table 3: Increase in the Private Debt Burden Due to Deflation: The Depression of 1921-22 vs. the Great Depression7
"... In PAGE 6: ... We measure the increase in the debt burden as the increase in the real value of debt (relative to output) due to deflation over the first two years of each depression. Table3 shows the initial stock of debt relative to output at the price level peak prior to each depression, as well as the percentage change in prices in the first two years of each depression, the implied percentage increase in the debt burden relative to initial output, and the percentage change in real output. The most striking feature of these data is that the 6Some economists have also suggested that high real interest rates were an important contributing factor to the Great Depression.... ..."
Table 13 Features of student-student talk
"... In PAGE 7: ...able 12 Components of situated cognition (after Wenger, 1998) ...........................................................42 Table13 Features of student-student talk.... ..."
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