### Table 4. Linear Least Squares Coefficients for VOCs Versus NOya

2003

"... In PAGE 6: ... The underestimate of NOy in the urban plume in the model base case is also evident in Figure 6. [28] Figure 7 and Table4 show the correlation between summed anthropogenic VOCs and NOy from measurements and from the standard model scenario along the aircraft trajectory for 17 July. Summed VOCs have been expressed as propene-equivalent carbon [Chameides et al.... In PAGE 7: ... Peak measured VOC levels are significantly lower than the model maximum values, but this may be the result of the relatively long averaging time (10 min) for the individual measure- ments. Measured VOCs are higher than model VOCs for equivalent NOy, but the slope between VOCs and NOy ( Table4 ) is slightly lower in the measurements than in the standard model scenario. [29] Unlike other VOCs, isoprene (Figure 8) does not correlate with NOy.... ..."

Cited by 1

### Table 3: Linear correlation of sampling at various rates

2005

"... In PAGE 4: ...f the full simulation. Variables with no data (i.e. variables that are only accessed outside the boundaries of a timestep) are given a zero memtime. The results in Table3 show these linear correlation coefficients for four different sam- pling rates, as compared to the total memtime (all cache levels) for every data structure.... ..."

Cited by 1

### Table 1: Linear Correlation Between yn and n

1998

"... In PAGE 11: ... Having done so, we computed the least squares t yn(E) log NE (E)n + (E) for n = 1; 2; : : : 15, for each curve E, and then for the curves in each subset we computed the maximum, minimum, and average of and . The results are listed in Table1 , together with the number of curves in each subset and the correlation coe cient which measures how well yn(E) log NE is linearly approximated by n.... In PAGE 12: ...or some small gt; 0. Then a calculation similar to the proof of Proposition 5.1 shows that log N ? 2 1 X n=1 2 n (1 + ) log N = 1 ? log N : Of course, in practice the estimate (5) would only hold for small values of n, but this computation shows that the net e ect will be to decrease the contribution of the R1(Et; ) apos;s to the average rank. On the other hand, the fact that the apos;s in Table1 are positive means that at least the rst few yn apos;s are larger than the theoretical value of 2 n= log N. This will have the e ect of increasing the value of the R1(Et; ) apos;s, thereby making the average rank somewhat larger.... ..."

Cited by 5

### TABLE I LINEAR CORRELATIONS BETWEEN MODIS CHANNELS

### TABLE V LOCAL LINEAR CORRELATIONS EMBEDDED IN THE DATASET

### Table 1: Comparison of WPC correlation and convolution with linear correlation

2000

Cited by 12

### Table 1: Comparison of WPC correlation and convolution with linear correlation

### Table 5. Linear correlation between predited and measured response time

2004

"... In PAGE 11: ... The linear relationship between predicted and measured response time is assessed to indicate the accuracy of the predicted optimal value. The calculation is shown in Table5 . The correlation coefficients of the three models indicate a good linear relationship between predicted and measured response time under the various settings of thread pool size.... ..."

Cited by 4

### Table 4, with various linear correlation coefficients, show that the original

1992