### Table 2. Key Data Patterns and Explanatory Models (subduction, buckling, rift)

"... In PAGE 13: ...elationships among patterns of data, especially those involving multiple variables (e.g., presence of earthquakes, absence of volcanoes, continental versus oceanic plates) and key ideas related to causal mechanisms. Table2 summarizes these key ideas. Table 2.... ..."

### Table 7. Explanatory variables in order of decreasing significance forming the best explanatory model for height growth.

2004

"... In PAGE 32: ... Table7 . Overview of methods and models.... ..."

### Table 3 Explanatory variables used in the regression model

in An Analysis Of Time Allocation, Departure Time And Route Choice Behavior Under Congestion Pricing

"... In PAGE 13: ...immediately before and after the trip, the 131 cases in which discretionary activities are engaged both before and after the trip are used to estimate the parameters (potential selectivity bias resulting from this is not examined in this study). The explanatory variables used are summarized in Table3 , and the estimation results are presented in Table 4. The constant is not included in the regression model because the explanatory variables are defined in terms of the differences between the activities before and after the trip and theoretically the model should not contain a constant term.... ..."

### Table 3 Explanatory variables used in the regression model

"... In PAGE 12: ... The 131 cases in which discretionary activities are engaged both before and after the trip are used to estimate the parameters. The explanatory variables used are summarized in Table3 , and the estimation results are presented in Table 4. Although the goodness of fit statistics, R 2 and 2 R , are not particularly high, the model as a whole is highly significant and the effects of the explanatory variables on time allocation behavior as... ..."

### Table 4 Overall fit and explanatory power of the models Recommended

in 786

"... In PAGE 16: ...2 Structural model results Following the satisfactory model evaluation results, this section will use SEM to examine and compare TRA, TPB and TAM to find which model performs well in explaining the internet banking behavior. Table4 summarizes the degree to which each model fit the data. ---INSERT TABLE 4 ABOUT HERE--- The fit statistics in Table 4 indicate that the TRA provides a poor fit to the data.... In PAGE 16: ... Table 4 summarizes the degree to which each model fit the data. ---INSERT TABLE 4 ABOUT HERE--- The fit statistics in Table4 indicate that the TRA provides a poor fit to the data. The fit of TPB is moderately comparable to the TAM with a slightly better RMSEA, suggesting that even when the increased complexity of the TPB is taken into consideration, the fit of the TPB model is at least equivalent to the TAM.... ..."

### Table 6. Regression Results for Resold Business Lines (RSLDBPLN) Explanatory Variable Model 6 Model 7

"... In PAGE 31: ... These results are generally comparable to the results of the entry models. Table6 shows the results for entrant market share using resold business services. Model 6 shows the results using all explanatory variables.... ..."

### Table 2 Comparisons of explanatory powers among eight models (source: Venkatesh

2006

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### Table 5. Regression Results for Output Using UNEs and Facilities (TRNKPLN) Explanatory Variable Model 4 Model 5

### Table 2: Comparing the explanatory power of regression trees and linear models.

"... In PAGE 5: ... We also constructed regression tree and linear models for subsets of the data cor- responding to the two different systems, Annie and Elvis. As shown in Table2 , the explanatory power of the tree models was quite similar to that obtained via linear models, and similar sets of predictor variables were observed. However, there is reason to be cautious with respect to the linear models.... ..."

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