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Practice of Kinetics (Comprehensive Chemical Kinetics, Volume 1)

Practice of Kinetics (Comprehensive Chemical Kinetics, Volume 1)

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2 CONSISTENCY WITH EQNS. OF TYPE -d[A]/dt = k[A]'[B]' 377The point is that, corresponding to the number <strong>of</strong> degrees <strong>of</strong> freedom used inestimating s(k;), we can look up in statistical tables the absolute value <strong>of</strong> t whichis exceeded with a given frequency. In this way, we can establish for a givenconfidence level the limits between which K; must lie. A numerical example takenfrom Appendix 2 will make this clearer.Example. Taking run 1 <strong>of</strong> the set <strong>of</strong> experiments, we find thatk; = 7.097 x sec-'s(k;) = 0.023 x sec-'n, = 22Our object in this example is to establish the 90% confidence limits for K;; thatis, the limits between which K; is likely to lie with 90% probability. From tables,90% <strong>of</strong> all values <strong>of</strong> t based on 20 degrees <strong>of</strong> freedom lie between f 1.72 and sothe 90 % confidence limits for the difference between k; and the population averagearef 1.72 x s(k;)= f0.w x sec"In other words, on the basis <strong>of</strong> the data <strong>of</strong> run 1, we expect with 90% confidencethat the 'true' value <strong>of</strong> the rate coefficient (the population average) will lie between(7.057 and 7.137) x sec- 'Returning to the general case, it should be obvious that, if we can establishconfidence limits for each value <strong>of</strong> k; , we can calculate confidence limits for eachvalue <strong>of</strong> the rate coefficient.(iii) Homogeneity <strong>of</strong> the rate coeficientsTo this point, our discussion has been confined to describing the methods <strong>of</strong>processing the data <strong>of</strong> a single run and to <strong>of</strong>fering some explanation <strong>of</strong> the significance<strong>of</strong> the derived quantities. However, we have repeatedly stressed that validconclusions can only be reached if the data <strong>of</strong> several runs are compared. It is thepurpose <strong>of</strong> this section to demonstrate how this comparison is best carried out.We have r values <strong>of</strong> the rate coefficient together with a corresponding number<strong>of</strong> estimates <strong>of</strong> standard error. This-set <strong>of</strong> values can be divided into u groupst These data are characterized by m, = 1 and so in this particular case ki can be identified withthe rate coefficient.References p. 407

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