Computed T Value

The T test value is a measure of the regression coefficient s significance, i.e., does the coefficient have a real meaning or should it be zero. The larger the absolute value of T the greater the probability that the coefficient is real and should be used for predictions. A T test value 1.7 or higher indicates that there is a high probability that the coefficient is real and the variable has an important effect upon the response. 

---

For the two or more lines involved in a determination, the slopes of the lines were tested for parallelism by computing the "t" value for each line and comparing it with the "t" value determined from the standard tables. If the computed t value exceeded the tabular value, the slopes were considered to differ significantly and the lines were rejected for use. If the computed t value was less than the tabular value, parallel lines were constructed by using weighted slopes to determine a common mean slope. The weighting factors were the reciprocals of the squared standard errors of the slopes. 

Table 5.18 contains the calculation concerning the significance of the regression coefficients from relation (5.110). However, respect to table 5.6, the rejection condition of the hypothesis has been changed so that we can compare the computed t value (tj) with the t value corresponding to the accepted significance level (t /2)-... 

The significance of the r-values can be determined by t-test, assuming that both variables are normally distributed and the observations are chosen randomly. The test compares the computed t-value with a tabulated t-value using the null hypothesis. In this study, a 95% level of confidence was chosen. If the computed t-value is greater than the tabulated t-value, the null hypothesis is rejected. This means that r is significant. If the computed t-value is less than the tabulated t-value, the null hypothesis is not rejected. In this case, r is... 

The rigid-rod calculation gave computed T, values shown in Table III, along with the experimental values. It is clear from this simulation that some other mode of motion must be included. Therefore, I show how a single two-state model for internal motion superimposed on independent, symmetric-top overall motion rationalizes the observed values at 30°C and how the model accounts in principle for the temperature-dependent T, changes. It is also shown that attempts to simulate observed values or linewidths using the same model fail, and a possible reason for the failure is given. 

Thermal resistance is the reciprocal of thermal conductance. It is expressed as m KTW. Since the purpose of thermal insulation is to resist heat flow, it is convenient to measure a material s performance in terms of its thermal resistance, which is calculated by dividing the thickness expressed in meters by the thermal conductivity. Being additive, thermal resistances facilitate the computation of overall thermal transmittance values (t/-values). 

The second separation method involves n.O.e. experiments in combination with non-selective relaxation-rate measurements. One example concerns the orientation of the anomeric hydroxyl group of molecule 2 in Me2SO solution. By measuring nonselective spin-lattice relaxation-rat s and n.0.e. values for OH-1, H-1, H-2, H-3, and H-4, and solving the system of Eq. 13, the various py values were calculated. Using these and the correlation time, t, obtained by C relaxation measurements, the various interproton distances were calculated. The distances between the ring protons of 2, as well as the computer-simulated values for the H-l,OH and H-2,OH distances was commensurate with a dihedral angle of 60 30° for the H-l-C-l-OH array, as had also been deduced by the deuterium-substitution method mentioned earlier. 

The general solution approach, to this type of problem, is illustrated by the information flow diagram, shown in Fig. 4.8. The integration thus starts with the initial values at Z = 0, and proceeds with the calculation of r, along the length of the reactor, using the computer updated values of T and Ca, which are also produced as outputs. 

To compute the value of rnet from Eq. (66), Eqs. (53), (54), and (56) must be used to evaluate T and Ts. The valuer of Fnet calculated from Eqs. (64) and (66) are within 2% for exposure times greater than 10 7 sec, and therefore they are plotted on the same curve in Fig. 10. The two values of rnet are given in Table I they agree within 1% over the entire range of TL. 

T, values can be easily determined using pulse sequences which form part of the standard computer software, the most common one being the so-called inversion-recovery experiment. 

We can see at once that each proton behaves differently, because it has its individual relaxation time Tx depending on the delay signals may be negative, positive, or have zero intensity. The T, values can be computed using spectrometer software. 

In reference to the tensile-strength table, consider the summary statistics x and x by days. For each day, the t statistic could be computed. If this were repeated over an extensive simulation and the resultant t quantities plotted in a frequency distribution, they would match the corresponding distribution of t values summarized in Table 3-6. 

Performing the calculations on function fitted to the raw data has another ramification the differences, and therefore the sums of squares, will depend on the units that the K-values are expressed in. It is preferable that functions with similar appearances give the same computed value of nonlinearity regardless of the scale. Therefore the sum-of-squares of the differences between the linear and the quadratic functions fitted to the data is divided by the sum-of-squares of the T-values that fall on the straight line fitted to the data. This cancels the units, and therefore the dependency of the calculation on the scale. 

The range of the synthetic data we want to generate should be such that the A-values have the same range as the original data. The reason for this is obvious when we apply the empirically derived quadratic function (found from the regression) to the data, to compute the T-values, those should fall on the same line, and in the same relationship to the X as the original data did. 

We compute the value of the van t Hoff factor, then use this value to compute the boiling point elevation. 

DFT theory even seems to improve the performance of MP2 in cases where there is some small contribution of non dynamic correlation. This is seemingly the case in the BP86 computed first dissociation energies of a variety of metal carbonyls [51]. For instance, in the case of Cr(CO)6, the BP86 value is 192 kJ/mol, in exact (probably fortuitous) agreement with the (computationally most accurate) CCSD(T) value of 192 kJ/mol, but also reasonably close to the experimental value of 154 8 kJ/mol. In this case, the GGA DFT result improves clearly the local DFT SVWN value of 260 kJ/mol, and the MP2 result, wich is 243 kJ/mol. Comparable results can be found for the optimization of the Os-O distance in OsC>4 [52], which is relevant concerning olefin dihydroxylation. 

By computation or by using tables of Bessel functions, values of CjA can be found for a range of irf2) /k /D t values. Let... 

The role of the input data NM, NX, NY and NP is obvious from the text and the remark lines, but the array T(NM,NX) of independent variables deserves some explanation. Each line of the array should contain all information that enables us to compute the value of the dependent variables for a sample paint at the current values of the parameters. Therefore, the module transfers the appropriate row of T(NM,NX) into the vector X(NX) for further use in the user supplied subroutine. This subroutine starting at line 900 computes the independent variables Y(NY) at the current parameters P(NP) and independent... 

---