Statistics interval

Hahn GJ and Meeker WQ (1991) Statistical Intervals a Guide for Practitioners. John Whey, NY. 

Statistical interval, e.g., of a mean, y, cnfCy ) = y ycnfwhich express the uncertainty of measured values. CIs are applied for significance tests and to establish quantities for limit values (CV). 

Hahn, G. J., Meeker, W. Q. Statistical intervals A guide for practitioners. Wiley Interscience, New York, 1991. 

GJ Hahn and WQ Meeker. Statistical Intervals. A Guide to Practitioners. John Wiley Sons, New York, NY, 1991. 

One can calculate the acceptance criteria for the particle size distribution median and standard deviation by use of a technique described by Hahn and Meeker (11). Their work describes three types of statistical interval confidence, prediction, and tolerance. The authors maintain that the choice of the appropriate statistical interval to use depends on the nature of the parameters to be estimated. The confidence interval is used when hying to find bounds on a population parameter—for example, the population mean or standard deviation. The confidence interval is the most commonly appearing of the three intervals and is the interval... 

J Hahn, WQ Meeker. Statistical Intervals A Guide for Practitioners. New York John Wiley, Sons 1991. 

Confidence interval A statistical interval estimate which is used to indicate the reliability of an estimate. 

Case 1. The particles are statistically distributed around the ring. Then, the number of escaping particles will be proportional both to the time interval (opening time) dt and to the total number of particles in the container. The result is a first-order rate law. 

One may justify the differential equation (A3.4.371 and equation (A3.4.401 again by a probability argument. The number of reacting particles VAc oc dc is proportional to the frequency of encounters between two particles and to the time interval dt. Since not every encounter leads to reaction, an additional reaction probability has to be introduced. The frequency of encounters is obtained by the following simple argument. Assuming a statistical distribution of particles, the probability for a given particle to occupy a... 

RRKM theory assumes a microcanonical ensemble of A vibrational/rotational states within the energy interval E E + dE, so that each of these states is populated statistically with an equal probability [4]. This assumption of a microcanonical distribution means that the unimolecular rate constant for A only depends on energy, and not on the maimer in which A is energized. If N(0) is the number of A molecules excited at / =... 

If all the resonance states which fomi a microcanonical ensemble have random i, and are thus intrinsically unassignable, a situation arises which is caWtA. statistical state-specific behaviour [95]. Since the wavefunction coefficients of the i / are Gaussian random variables when projected onto (]). basis fiinctions for any zero-order representation [96], the distribution of the state-specific rate constants will be as statistical as possible. If these within the energy interval E E+ AE fomi a conthuious distribution, Levine [97] has argued that the probability of a particular k is given by the Porter-Thomas [98] distribution... 

IVR in tlie example of the CH clnomophore in CHF is thus at the origin of a redistribution process which is, despite its coherent nature, of a statistical character. In CHD, the dynamics after excitation of the stretching manifold reveals a less complete redistribution process in the same time interval [97]. The reason for this is a smaller effective coupling constant between the Fenni modes of CHD (by a factor of four) when... 

Analysis of tlie global statistics of protein sequences has recently allowed light to be shed on anotlier puzzle, tliat of tlie origin of extant sequences [170]. One proposition is tliat proteins evolved from random amino acid chains, which predict tliat tlieir length distribution is a combination of the exponentially distributed random variable giving tlie intervals between start and stop codons, and tlie probability tliat a given sequence can fold up to fonii a compact... 

It is appropriate to consider first the question of what kind of accuracy is expected from a simulation. In molecular dynamics (MD) very small perturbations to initial conditions grow exponentially in time until they completely overwhelm the trajectory itself. Hence, it is inappropriate to expect that accurate trajectories be computed for more than a short time interval. Rather it is expected only that the trajectories have the correct statistical properties, which is sensible if, for example, the initial velocities are randomly generated from a Maxwell distribution. 

The integral of the Gaussian distribution function does not exist in closed form over an arbitrary interval, but it is a simple matter to calculate the value of p(z) for any value of z, hence numerical integration is appropriate. Like the test function, f x) = 100 — x, the accepted value (Young, 1962) of the definite integral (1-23) is approached rapidly by Simpson s rule. We have obtained four-place accuracy or better at millisecond run time. For many applications in applied probability and statistics, four significant figures are more than can be supported by the data. 

The distribution of the /-statistic (x — /ji)s is symmetrical about zero and is a function of the degrees of freedom. Limits assigned to the distance on either side of /x are called confidence limits. The percentage probability that /x lies within this interval is called the confidence level. The level of significance or error probability (100 — confidence level or 100 — a) is the percent probability that /X will lie outside the confidence interval, and represents the chances of being incorrect in stating that /X lies within the confidence interval. Values of t are in Table 2.27 for any desired degrees of freedom and various confidence levels. 

The F statistic, along with the z, t, and statistics, constitute the group that are thought of as fundamental statistics. Collectively they describe all the relationships that can exist between means and standard deviations. To perform an F test, we must first verify the randomness and independence of the errors. If erf = cr, then s ls2 will be distributed properly as the F statistic. If the calculated F is outside the confidence interval chosen for that statistic, then this is evidence that a F 2. 

The probabilistic nature of a confidence interval provides an opportunity to ask and answer questions comparing a sample s mean or variance to either the accepted values for its population or similar values obtained for other samples. For example, confidence intervals can be used to answer questions such as Does a newly developed method for the analysis of cholesterol in blood give results that are significantly different from those obtained when using a standard method or Is there a significant variation in the chemical composition of rainwater collected at different sites downwind from a coalburning utility plant In this section we introduce a general approach to the statistical analysis of data. Specific statistical methods of analysis are covered in Section 4F. 

The equation for the test (experimental) statistic, fexp, is derived from the confidence interval for p,... 

The "feedback loop in the analytical approach is maintained by a quality assurance program (Figure 15.1), whose objective is to control systematic and random sources of error.The underlying assumption of a quality assurance program is that results obtained when an analytical system is in statistical control are free of bias and are characterized by well-defined confidence intervals. When used properly, a quality assurance program identifies the practices necessary to bring a system into statistical control, allows us to determine if the system remains in statistical control, and suggests a course of corrective action when the system has fallen out of statistical control. 

Use zero-order Markov statistics to evaluate the probability of isotactic, syndio-tactic, and heterotactic triads for the series of p values spaced at intervals of... 

Flavor Intensity. In most sensory tests, a person is asked to associate a name or a number with his perceptions of a substance he sniffed or tasted. The set from which these names or numbers are chosen is called a scale. The four general types of scales are nominal, ordinal, interval, and ratio (17). Each has different properties and allowable statistics (4,14). The measurement of flavor intensity, unlike the evaluation of quaUty, requires an ordered scale, the simplest of which is an ordinal scale. 

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