### Table 2. U.S. Meat Demand Models: Thep-values for Equation-by-Equation System Misspe- cification Tests a

"... In PAGE 12: ... Table2 . Continued Model C Model D Item Beef Pork Chicken Beef Pork Chicken Individual Tests Normality Functional Form: RESET2 KG2 Heteroskedasticity:b Static Beef RESET2 Pork Chicken Static Beef WHITE Pork Chicken Dynamic Beef Pork Chicken Autocorrelation Parameter Stability: Variance Mean Joint Tests Overall Mean Test Parameter Stability Functional Form Autocorrelation Overall Variance Test:b Beef Pork Chicken Parameter Stability: Beef Pork Chicken Static Heteroskedasticity: Beef Pork Chicken Dynamic Heteroskedasticity: Beef Pork Chicken 0.... ..."

### Table 2 summarizes the testable implications of different hypotheses. Note that the hypotheses

2002

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### Table 7: Structure of the Analysis of Variance Table, for Single Factor Response Surface Regression

"... In PAGE 19: ... In a similar fashion, a confidence interval for our estimate of can be defined by noting that the standardized value for b should be t-distributed: (71) where is the true value for , so that (72) Regression results should also be framed in an analysis of variance framework. In the simple one factor case, a simple ANOVA table might be as shown in Table7 . In this case, is the sum of squared values of the estimates, and is an estimate of the variance explained by the model, where our model is purely linear (no intercept term) as given in Eq.... ..."

### Table 1 Summary of Model Structures

2006

"... In PAGE 8: ... Again the case ut = vt = zt is formally identical and could be used to facilitate hierarchical priors. Table1 summarizes the five structures we consider in subsequent examples in this study. 2.... In PAGE 14: ... Various configurations of k, p and q lead to the model variants A through E discussed at the end of Section 2.2 and detailed in Table1 . This expression facilitates comparison of the smoothly mixing regression model with closely related approaches to modeling p(y | x) found in the literature, which tend to be special or limiting cases of (19).... In PAGE 17: ... Specifications B and E are superior to specification D in nearly all cases. Unlike any of the other specifications, B and E incorporate mixtures of linear combi- nations of covariates, and the results in Table1 may be taken as evidence that this is important in modeling the distribution of earnings conditional on age and education. There is no systematic tendency for one of these specifications to outperform the other.... In PAGE 18: ...isons of these specifications in Table1 indicate that the models have similar implica- tions for the distribution of earnings conditional on age and education. We examine this implication by reproducing in Figure 5 the same quantiles as in Figure 3, except that these quantiles come from model B rather than from model E.... ..."

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### Table 5. Structural Equations for the Bone Geometry Model

2006

"... In PAGE 8: ... We employed an additive genetic model in the SEM because all QTL effects showed intralocus additivity. We developed an initial SEM (Figure 7 and Table5 ) following the steps of model formulation, assessment, and refinement described in Materials and Methods. In order to resolve the causal relationships among the three phenotypes, we exam- ined the complete set of 11 models listed in Figure 8.... In PAGE 8: ... The graphical SEM is shown in Figure 7. Path coefficients and t-statistics are summarized in Table5 . The model explains 67.... In PAGE 9: ... Structural Equation Model for Bone Geometry Genetic effects have been grouped. Sign and magnitude of path coefficients can be found in Table5 . Group Q1 includes loci with effects that are specific to PCIR (Q4@66, Q5@84, Q6@32, Q7@50, and Q11@68).... ..."

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### Table 2: Bounds on the number of solutions of the FKP for a robot with planar platform (9 unknowns) When there are more than 3 sensors (we always assume that the sensors are not redundant, which means that they actually give information), it is not interesting to build the dialytic matrix. Indeed it is better to solve the non-linear system by taking advantage of its structure when the linear equations have been eliminated. We obtain in this way a better bound. If 6 sensors are used and give information, we obtain a unique solution by solving the linear system corresponding to the 6 equations of type IV given by the sensors and the 3 equations of type III. The cpu times given in Table 3 are the times we needed to obtain the bound with the symbolic method of Section 3.3. They are only indicative. In fact in practice this computation is not done since we only want to compute numerically the result.

"... In PAGE 26: ... Sensors 3 2 1 Before linear elimination 28 After linear elimination 10 15 21 Table 1: Number of monomials present in the equations before and after the resolu tion of the linear equations 5.1 Planar platform Table2 gives bounds on the number of solutions, depending on the number of extra sensors that are added on the robot. They also give the number of unknowns in the initial non-linear system, the number of equations in the square system obtained by... ..."

### Table 4 Parameter Estimates for Structural Equation Model

"... In PAGE 23: ...22 Results of hypotheses tests Our findings, shown in Table4 , column D, are as follows.... In PAGE 25: ...10, and z-statistic of 1.645, we dropped several paths, as shown in Table4 , column E. We however retain paths involving control variables even if their coefficients were insignificant.... In PAGE 28: ...05. Lastly, in Table4 , column F, the parameters of moderating effects are statistically not significant. What results mean Our results highlight important aspects of buyer supplier knowledge transfer interfaces.... ..."

### Table 3 : Comparison of registers controllability/observability Without testability With testability

"... In PAGE 13: ... The interested reader can refer to [25] for details on gain computation. Table3 gives the results of register allocation for the Differential Equation example after an allocation taking no account of testability (i.e.... ..."

### Table 1: Implications for Other Work Packages

2007

"... In PAGE 6: ...1_V1_MS.doc PUBLIC Page VI Table of Figures Figure 1: SUPER Architecture Diagram 1 Figure 2: SUPER SBPM Lifecycle 2 Figure 3: SUPER Modelling Stack 3 Figure 4: Semantic Business Process Management (SBPM): Overview 5 Figure 5: Process Lifecycle in SBPM 6 Figure 6: Origins of Process Models for SBPM 7 Figure 7: Process Modelling Ontologies in SUPER 8 Figure 8: UML Representation of DDPO 13 Figure 9: sEPC Class Hierarchy 19 Figure 10: Example Process EPC Model 22 Figure 11: sBPMN Class Hierarchy 25 Figure 12: Example Process BPMN Model 27 Figure 13: BPMO Class Hierarchy 29 Figure 14: sBPEL Class Hierarchy 35 Figure 15: sBPEL Process 36 Figure 16: sBPEL Semantic Invoke 37 Figure 17: sBPEL Activity 37 Figure 18: sBPEL Basic Activity 38 Figure 19: sBPEL Structured Activity 38 Figure 20: sBPEL Invoke 39 Figure 21: EVO Class Hierarchy 41 Figure 22: PMO Dependencies 45 Figure 23: PMO Snapshot 46 Figure 24 UML Representation of Events Ontology (EVO) 53 List of Tables Table1 : Implications for Other Work Packages 48 ... ..."