Secondly, with the matrix of error covariance and observability of the system, this paper designs a vector-form information sharing algorithm so that each state variable can get a different coefficient.
We get a correlation coefficient of 0.86: still falling in the range of very strong positive correlation, though the absolute value of the correlation coefficient is lower than the correlation coefficient values obtained with the Pearson's and Spearman's measures.
The central loops are scored as follows: (i) to get a location coefficient according to the locations of loops: 1 for standard loop, 0.75 for type II decentered loop, 0 or 0.5 for type I decentered loops starting before nt7 inclusive or at nt8; (ii) to get the size coefficient according to the sizes of the loops: 10 for 1 bp, 20 for 2 bp, and 25 for 3 bp.
Therefore, even with perfect predictive power, we would not get a correlation coefficient of one.
This value was averaged across all nodes to get a mean clustering coefficient for the entire network.
If we correlate these chemical-shift changes with those from the binding of lactose, we get a linear regression coefficient of 0.82, which indicates that Gal-β(1→4 -Gal interacts primarily at the same site on gal-1→4 -Galtose.
Here is an example: The exact solution with pi: Approximate with the decimal: Generally speaking, you will not get a whole number coefficient for pi.
The p-values associated with the correlation coefficients (namely the probabilities of getting a correlation coefficient that is greater than or equal to that value for random i. i. d. gaussian data) in table 1 are as follows: r = 0.48 => p = 13.8%, r = 0.71 => p = 3.7%, r = 0.86 => p = 0.65%.
Using this process, we get a list of the coefficients (one coefficient for each document in development data set) expressing distance to the target domain.
When comparing duplex free energies as computed by RIsearch and DuplexFold (Fig. 3b), we get a Pearson product-moment correlation coefficient r of 0.99 (P-value 2.2e−16).
