E statistic

Deming, W. E. Statistical Adjustment of Data. Wiley New York, 1943 (republished by Dover New York, 1961) p. 171. 

Deming, W.E. "Statistical Adjustment of Data" Wiley New York, 1943 p 202. (Dover Publications reprint, 1964.)... 

Models should be assessed not only in terms of their goodness of fit (i.e., statistical quality) but also in terms of their predictive power. The predictive power of a model can be assessed only by estimating the activity of a set of compounds not included in the original model. 

Landau, L. Lifschitz, E., Statistical Mechanics, Pergamon New York, 1980... 

Mullins, E., Statistics for the Quality Control Chemistry Laboratory, ISBN 0-85404-671-2, The Royal Society of Chemistry, Cambridge, UK, 2003. 

Huet, S., Bouvier, A., Poursat, M. A., Jolivet, E. Statistical Tools for Nonlinear Regression. Springer, New York, 2003. 

E. Statistical Survey of Smelt-Water Boiler Explosions. 148... 

If, as above, the potential-energy barrier height is E, statistical mechanical considerations indicate that the atom will have sufficient thermal energy to pass over the barrier a fraction exp(— E/k T) of the time. If/is a characteristic atomic vibrational frequency, the probability p that during unit time the atom will pass the potential-energy barrier is given by... 

Given an approximation to V( 0s ) acceptable for the purposes at hand, one can proceed to compute equilibrium, i.e., statistically mechanically averaged, values

for properties P( 0s ) of interest using standard procedures which weight each conformation of the carbohydrate molecule by the Boltzmann factor of V( 0s ) normalized by the configuration integral given in eqn. (9). 

Transition state theory yields rate coefficients at the high-pressure limit (i.e., statistical equilibrium). For reactions that are pressure-dependent, more sophisticated methods such as RRKM rate calculations coupled with master equation calculations (to estimate collisional energy transfer) allow for estimation of low-pressure rates. Rate coefficients obtained over a range of temperatures can be used to obtain two- and three-parameter Arrhenius expressions ... 

Meier, PC., Ziind, R.E. Statistical Methods in Analytical Chemistry, Wiley, New York, Chichester, Brisbane, Toronto, Singapore, 1993... 

Ed ) Handbook of Environmental Chemistry, Vol. 2, Part H, Springer, Berlin, 1995 Haccon, E, Mellis, E. Statistical Analysis of Behavioural Data, Oxford University Press, Oxford, New York, Tokyo, 1994... 

For each TCP/IP connection, 41 various quantitative and qualitative features were extracted. Table 2 shows the list of 41 features of the data [14], This features can be divided into three categories intrinsic features, i.e., general information related to the connection traffic features, i.e., statistics related to past connections similar to the current one e.g., number of connections with the same destination host or connections related to the same service in a given time window or within a predefined number of past connections content features, i. e., features containing information about the... 

Journals tend to favor accepting studies showing positive outcomes, i.e., statistically significant differences between treatments, in favor of negative outcomes (as just noted in Piantadosi s definition). 

The statistical polymer method proposed recently by the author [6] considers polymeric systems as sets of assemblages possessing structures averaged over all these of polymers containing the same numbers of monomeric units, i.e., statistical polymers. For the case when one is interested in the evaluation of the weight distribution and/or additive (extensive) parameters like energy, entropy, etc., one can consider statistical polymers instead branched cross-linked ones. 

Fuite, J., Marsh, R., and Tuszynski, J., Fractal pharmacokinetics of the drug mibefradil in the liver, Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, Vol. 66, No. 2, 2002, pp. 021904. 

In addition to chance, systematic biases can also affect the relationship between an exposure and disease. Biases lead to an incorrect estimate of the relationship between the exposure and disease that is an incorrect measure of the relative risk. Some biases will result in an effect being observed (i.e., statistically significant RR) when there is not a causal relationship, whereas other biases will result in obscuring a causal relationship between exposure and disease (refer to as biasing toward the null hypothesis). In an individual study, biases can be introduced during the selection of the subjects, follow-up of disease status, or exposure assessment. Biases can also occur in the evaluation of a causal relationship across studies. 

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