### Table 6 Regression analysis of influence of success factors on success Success factors All Linear Nonlinear

"... In PAGE 14: ...variables were excluded from further analysis. To investigate importance of success factors on success of business ventures, regression analysis was performed (refer to Table6 ). Influence of success factors in total has very significant explanatory power for success of business venture (above 48% of R2 and F value of 4.... ..."

### TABLES Table 1. Global image properties in ascending order of proportion of variance R2 in color- constancy index explained by a linear regression on the corresponding statistic. Global property Statistic R2 (%) SE (%)

### Table 5 TNT Distribution Coefficients Kd, L/kg, and Associated Linear Regression Coefficients r2 for Homoionic and Untreated Aquifer Soils and Clays

"... In PAGE 24: ...ad weakly ob Figure 8. T s and Discussion 17 Cation Adsorption Homoionic saturation of aquifer soils and clays had a marked effect on TNT sorption coefficients ( Table5 ). Saturation of clays and aquifer soils with the hydrated cations K+ and NH4+ resulted in increased adsorption of TNT compared with that of the strongly hydrated cation Ca++.... In PAGE 26: ...These results demonstrate that the processes observed for pure clay minerals (Haderlein, Weissmahr, and Schwarzenbach 1996) are also operative in aquifer soils. The linear adsorption model adequately fit the data as shown by the high regression coefficients in Table5 . This is not unexpected since linear adsorption isotherms successfully describe the adsorption of explosives compounds on mineral surfaces and soils under conditions of low surface coverage (Haderlein, Weissmahr, and Schwarzenbach 1996; Pennington et al.... In PAGE 26: ... However, Ca++ saturation resulted in a Kd well below those resulting from K+ and NH4+ saturation or the untreated montmorillonite. Specific adsorption of RDX was increased by homoionic substitution with K+ and NH4+, but HMX adsorption decreased under these same conditions and increased with homoionic substitution of Ca++ ( Table5 ). RDX adsorption on LAAP-D soils showed the same general trends as TNT (Figure 11), although the magnitude of increased adsorption is much greater for TNT than for RDX.... ..."

### Table 5. Results of analyses comparing alternative multiple regression models prediction of controller activity.

2006

"... In PAGE 9: ... We used a method that allowed us to compare specific regression models instead of an analysis such as stepwise linear regression because we wanted to assess the relative contribution of specific variables to the model rather than simply those variables that made statistically significant contributions, such as would result when conducting a multiple regression analysis. Table5 shows the results of these analyses. Row 1 shows the multiple correlation of the full model containing all three predictor variables (Number of Aircraft, Complexity Rating, and Complexity Value) with the criterion variable (number of R and RA controller data entries).... ..."

### Table 5: Parameters of linear regression models

"... In PAGE 8: ... Financial data for 2001 has not been taken into account, because correlation analysis has not indicated any information about the expected relationships between variables. Table5 . shows the results of tested regression models based on their crucial characteristics: coefficient of determination R2 (indicating how much of the variance between predicted and actual values does the model actually explain); adjusted coefficient of determination R2 (deemed as a more conservative estimate than the previously described one); F-test significance (which indicates the statistical significance/acceptability of the entire model); and colinearity index (whose value may indicate the problem of strong correlation of independent variables, which would be a significant violations of the assumptions behind the statistical model).... ..."

### Table 5: Parsimonious linear regression models for estimating coefficients p and q

2003

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### Table 3: R2 Values of Linear Regression Analyses of Perceptual Interpolation

### Table 2: LPY Slope Coefficients and R2 Statistics

2002

"... In PAGE 18: ...lways in excess of 0.91 for all maturities and usually in excess of 0.95. The linear regression coefficients of yield changes on the slope of the yield curve are presented in Table2 , and they are negative, significant, and increasing (in absolute value) with maturity, as documented in Campbell and Shiller (1991) and discussed in DS2. The regressions of yield change variances on lagged values of the level, slope, and 12We would like to thank Greg Duffee for making his data available on his web page.... ..."

### Table 3: Results of Linear Regression for Predicting Combination apos;s Average Precision (r2=0.94)

1998

"... In PAGE 4: ... Finally, the actual coe cients of the regression equation are normalized based on the distributions of the indi- vidual predictors, so that their magnitude can also be compared. 3 Results and Discussion Table3 presents the results of the multiple regression. Measures are sorted by decreasing F value, indicating roughly how important each measure is in predicting the average precision of the optimally combined system.... In PAGE 4: ... The clustering of points around the line y = x indicates a good t. p1; J1; p2; J2 : The upper part of Table3 indicates that in order to maximize average precision, one IR system must be very good (the normalized coe cient for p1 is positive and much larger than any other, and J1 apos;s coef- cient is also positive) but the second IR system should also be good (J2 apos;s coe cient is positive). Examining the actual combined systems supports this conclusion: the six best mixtures of IR systems (when averaged over all 20 queries) are all comprised of systems which are ranked among the top 10 individually.... In PAGE 4: ...94) the next paragraph. U1; U2; Orel; Ononrel : Another interesting conclusion from Table3 is that maximal precision can be achieved by minimizing the percentage of relevant documents which are unique to each system (U1 and U2 have negative coef- cients). This indicates exploitation of the Chorus E ect.... ..."

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