### Table 1. The Calculated Results for Analyzed Data-Set

2000

"... In PAGE 9: ... In order to have easy interpretable models, we have fixed the maximal number of terms in the equation to be equal to 8 and the maximum degree of polynoms to be equal to 3. The calculations performed using the select params option of the ANALYSIS are summarized in Table1 . The number of stored models was 3.... In PAGE 9: ... It was shown that the use of significant variables, as detected by MUSEUM, = improved PLS results (compare data in column 7 vs. column 6 in Table1 ). The similar tendency was also observed if only variables found to be relevant by the PNN algorithm were used in the cross-validation calculations (compare the last and 7 columns of Table 1).... In PAGE 12: ... b Number of significant PLS components. c The cross-validated q2 calculated using input variables optimized by MUSEUM approach (unless not stated otherwise the PLS results are from Table1 and 15 of (2)). d Number of input variables selected by PNN.... ..."

Cited by 2

### Table 4: Non-linear Test Results

2007

"... In PAGE 12: ... However, for all models, in both time periods, the np test suggests that there is no cointegration, while the kpss test suggests that there is. Table4 presents the results of the various random field based tests for nonlin- earity. For the first time period, 1959-1972, the tests nearly always reject the null hypothesis of linearity.... In PAGE 13: ...odels. The results are interesting and need careful interpretation. The most ob- vious result is that in the second period, it proved impossible to get the numerical optimisation algorithms to converge for Model 1 when no trend was present, and for Model 2 when a trend was present. It is for these two models that the tests for nonlinearity, reported in Table4 , often fail to reject the null hypothesis of linearity. Also, from Table 3, it is the no-trend version of Model 1 that is more likely to be a cointegrating relationship, according to the results of the adf test.... ..."

### Table 6 displays the parsimonious non-linear models based on the

"... In PAGE 28: ...Draft as of: 01/17/99 Page 28 of 34 Table6 : Parsimonious Models (GMDH) High School Model Adjusted R 2 = .638 Elementary School Model Adjusted R 2 = .... ..."

### Table 6 displays the parsimonious non-linear models based on the

1998

"... In PAGE 28: ...Draft as of: 01/17/99 Page 28 of 34 Table6 : Parsimonious Models (GMDH) High School Model Adjusted R 2 = .638 Elementary School Model Adjusted R 2 = .... ..."

### Table 1: Comparison of the EWMA Controller, the DHOBE-MR Controller and the DHOBE-SV Controller for Linear Perfect CMP Model

"... In PAGE 13: ...odel error exist. The weight of the EWMA controller is selected as 0.6 in this case. The simulation results are illustrated in Figure 5 and Figure 6 respectively for the comparison of the DHOBE-MR and DHOBE-SV method with the EWMA method. From Table1 , it can be seen that the compensation e ect of the DHOBE algorithm based controllers has no big di erence with that of the EWMA controller as measured by the MSE and standard deviation.... ..."

### 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: Rule base for predictive FLC

"... In PAGE 4: ...able 4: Overview of the control strategy......................................................................... 21 Table5 : Rule base for predictive FLC.... In PAGE 25: ...Table 5: Rule base for predictive FLC Table5 shows the general trend of the rules in the controller. Detailed non-linear behavior is implemented in the actual rule base, which is an 11x11 matrix.... ..."

### TABLE 2. The mean squared error (MSE) between the desired values and the predicted results for National Economy Growth Rate up to 19 years from 1982 to 2000.

### TABLE 1. The mean squared error (MSE) between the desired values and the predicted results for International Stock Price Indexes up to 31 months from January 1999 to July 2001. (unit=105)

### Table 4: Parameter estimates and standard errors of age and age-squared in OLS regression models predicting the logarithm of annual earnings (men and women combined).

2006

"... In PAGE 16: ... In sum, thinking in terms of status as well as of class does not appear to add a great deal to our understanding of differences in age-earnings curves. To check on this impression more formally, we show in Table4 results from analyses, based on the same data as used in Figure 1, in which we regress earnings on age and age-squared. It is evident from the first panel of Table 4 that, as would be expected, the coefficients for both age terms are significantly larger for Classes I and II than for Class V+VI+VII.... In PAGE 16: ... To check on this impression more formally, we show in Table 4 results from analyses, based on the same data as used in Figure 1, in which we regress earnings on age and age-squared. It is evident from the first panel of Table4 that, as would be expected, the coefficients for both age terms are significantly larger for Classes I and II than for Class V+VI+VII. But, from the second panel, it can be seen that, while the coefficients for status band 1 are larger than those for status bands 2, 3 and 4, there is far less differentiation among the latter.... ..."

Cited by 2