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Sampling size

Sample size is 100 ml and distillation conditions are specified according to the type of sample. Temperature and volume of condensate are taken simultaneously and the test results are calculated and reported as boiling temperature as a function of the volume recovered as shown in Table 2.1. [Pg.18]

MW frequency of 10 Hz. There are various considerations that influence the choice of the radiation frequency. Higher frequencies, which require higher magnetic fields, give inlierently greater sensitivity by virtue of a more favourable Boltzmaim factor (see equation (b 1.15.11)). However, several factors place limits on the frequency employed, so that frequencies in the MW region of the electromagnetic spectrum remain favoured. One limitation is the sample size at frequencies around 40 GHz the dimensions of a typical... [Pg.1558]

Diffraction is not limited to periodic structures [1]. Non-periodic imperfections such as defects or vibrations, as well as sample-size or domain effects, are inevitable in practice but do not cause much difSculty or can be taken into account when studying the ordered part of a structure. Some other forms of disorder can also be handled quite well in their own right, such as lattice-gas disorder in which a given site in the unit cell is randomly occupied with less than 100% probability. At surfaces, lattice-gas disorder is very connnon when atoms or molecules are adsorbed on a substrate. The local adsorption structure in the given site can be studied in detail. [Pg.1752]

In the next several sections, the theoretical distributions and tests of significance will be examined beginning with Student s distribution or t test. If the data contained only random (or chance) errors, the cumulative estimates x and 5- would gradually approach the limits p and cr. The distribution of results would be normally distributed with mean p and standard deviation cr. Were the true mean of the infinite population known, it would also have some symmetrical type of distribution centered around p. However, it would be expected that the dispersion or spread of this dispersion about the mean would depend on the sample size. [Pg.197]

Diagonal lines connecting the two axes show combinations of sample size and concentration of analyte containing the same absolute amount of analyte. As shown in Figure 3.6, for example, a 1-g sample containing 1% analyte has the same amount of analyte (0.010 g) as a 100-mg sample containing 10% analyte or a 10-mg sample containing 100% analyte. [Pg.43]

Vitha, M. F. Carr, P. W. A Laboratory Exercise in Statistical Analysis of Data, /. Chem. Educ. 1997, 74, 998-1000. Students determine the average weight of vitamin E pills using several different methods (one at a time, in sets of ten pills, and in sets of 100 pills). The data collected by the class are pooled together, plotted as histograms, and compared with results predicted by a normal distribution. The histograms and standard deviations for the pooled data also show the effect of sample size on the standard error of the mean. [Pg.98]

Sampling of a large population n = 900) of colored candies (M M s work well) is used to demonstrate the importance of sample size in determining the concentration of species at several different concentration levels. This experiment is similar to the preceding one described by Bauer but incorporates several analytes. [Pg.225]

Finally, we note that the size and shape of the particles of the packing, the packing technique, and column dimensions and configuration are additional factors which influence a GPC experiment. In addition, the flow rate, the sample size, the sample concentration, the solvent, and the temperature must all be optimized. Details concerning these considerations are found in analytical chemistry references, as well as in the technical literature of instrument manufacturers. [Pg.652]

Barcol Indenter. The Barcol hardness tester is a hand-held, spring-loaded instmment with a steel indenter developed for use on hard plastics and soft metals (ASTM D2583) (2). In use the indenter is forced into the sample surface and a hardness number is read direcdy off the integral dial indicator caUbrated on a 0 to 100 scale. Barcol hardness numbers do not relate to nor can they be converted to other hardness scales. The Barcol instmment is caUbrated at each use by indenting an aluminum ahoy standard disk suppHed with it. The Barcol test is relatively insensitive to surface condition but may be affected by test sample size and thickness. [Pg.467]

Numerous collections of herbicide analysis methods have been pubUshed (276—279). An increased emphasis has been placed on the first step in the environmental sampling process, that of obtaining a representative, uncontaminated sample. If this is to be accompUshed, consideration must be made of such factors as sample size and location (280—283). After the sample has been obtained, it must be stored in such a way as to minimize degradation. This generally consists of refrigeration, possibly preceded by some type of drying (284). [Pg.49]

Success Testing. Acceptance life tests ate sometimes planned with no failures allowed. This gives the smallest sample size necessary to demonstrate a rehabiUty at a given confidence level The rehabiUty is demonstrated relative to the test employed and the testing period. [Pg.15]

LLDPE can present a certain health hazard when it bums, since smoke, fumes, and toxic decomposition products are sometimes formed in the process. Exposure to burning LLDPE can cause irritation of the skin, eyes, and mucous membranes of the nose and throat due to the presence of acrolein and formaldehyde (81). Toxicity of LLDPE pyrolysis products depends on temperature, heating rate, and the sample size (82—84). [Pg.404]

With the Monte Carlo method, the sample is taken to be a cubic lattice consisting of 70 x 70 x 70 sites with intersite distance of 0.6 nm. By applying a periodic boundary condition, an effective sample size up to 8000 sites (equivalent to 4.8-p.m long) can be generated in the field direction (37,39). Carrier transport is simulated by a random walk in the test system under the action of a bias field. The simulation results successfully explain many of the experimental findings, notably the field and temperature dependence of hole mobilities (37,39). [Pg.411]

The quantity of sample required comprises two parts the volume and the statistical sample size. The sample volume is selected to permit completion of all required analytical procedures. The sample size is the necessary number of samples taken from a stream to characterize the lot. Sound statistical practices are not always feasible either physically or economically in industry because of cost or accessibiUty. In most sampling procedures, samples are taken at different levels and locations to form a composite sample. If some prior estimate of the population mean, and population standard deviation. O, are known or may be estimated, then the difference between that mean and the mean, x, in a sample of n items is given by the following ... [Pg.298]

If the standard deviation of the lot caimot be estimated, a sampling program of greater sample size is required to generate an estimate of the standard deviation for future sampling operations. In some cases, sample size can be increased and sampling costs reduced by the use of automatic samplers. These offer a substantial reduction in labor costs but an increase in capital costs. [Pg.298]

Technique Detection limits, ppb Precision, % Sample size, mL Econ omy Multie lemen t Dyna mic range Matri X interfe rence Spectr al interfe rence Refrac tories... [Pg.317]

Instead of calculations, practical work can be done with scale models (33). In any case, calculations should be checked wherever possible by experimental methods. Using a Monte Carlo method, for example, on a shape that was not measured experimentaUy, the sample size in the computation was aUowed to degrade in such a way that the results of the computation were inaccurate (see Fig. 8) (30,31). Reversing the computation or augmenting the sample size as the calculation proceeds can reveal or eliminate this source of error. [Pg.374]

This separation technique has been employed primarily for preparative types of separations because detailed knowledge of the properties of the sample is required. Also, because this separation results in discrete zones of sample ions which are virtually pure, it makes sense to use this technique when the sample size is large. This technique is ineffective when the levels of impurities are small with respect to the target compound small amounts of sample ions do not form zones well and tend to mix with the target compound. Information on this technique is available (30). [Pg.182]

In applications sample sizes are usually small and O unknown. In these cases, the t distribution can be used where... [Pg.492]

Since the t distribution relies on the sample standard deviation. s, the resultant distribution will differ according to the sample size n. To designate this difference, the respec tive distributions are classified according to what are called the degrees of freedom and abbreviated as df. In simple problems, the df are just the sample size minus I. In more complicated applications the df can be different. In general, degrees of freedom are the number of quantities minus the number of constraints. For example, four numbers in a square which must have row and column sums equal to zero have only one df, i.e., four numbers minus three constraints (the fourth constraint is redundant). [Pg.492]


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Accelerator mass spectrometry sample size

Aliphatic polymer sample size

Analytical method sample size

Assessing sample size

Assumptions sample size

Average content sample size

Bayesian statistics sample size

Binary data sample size

Biostatistics sample size

Blinding sample size

Calculating necessary sample size

Calculating sample size

Characteristic sample size

Choosing the Variables Needed for Sample-Size Estimation

Clinical trials sample size

Clinically relevant difference sample size

Clinically relevant difference with sample size

Cloud samples particle size distribution

Column optimum sample size

Confidence intervals sample size

Continuous data sample size

Cost-effective sample size

Cost-effective sample size model

Determining the Sample Size

Differential scanning calorimetry sample size

Dispersing Powder Samples for Size Characterization Studies

Dropouts sample size

Effective sample size

Effects of Phase Separation, Sample Preparation, Grain Size

Elimination sample size

Error types sample size

Errors particle size, sampling

Event rates sample size

Experimental error sample size

False negatives, power and necessary sample sizes

Fixed-sample size trials

HETP vs sample size

How to Estimate Correct Sample Sizes

Indium sample size

Influence of Sample Size

Infrared microspectroscopy small-size samples

Liquid chromatography sample size

Mass spectrometry sample size

Melting sample size dependence

Minimum sample size

Nondestructive analytical techniques sample size

Offsetting sample size against standard deviation

Particle size blending powder samples

Particle size sample

Particle size sampling technique

Particle size, measurement sample preparation

Polymers structure complexity sample sizes

Power and sample size

Power sample size

Power, sample size, experimental design

Primary endpoints sample size

Problem of small sample size

Process control sample size selection

Process development sample size

Protocol sample size

Questionnaires sample sizes

Random variable sample size

Reduced sample size, definition

Reporting the sample size calculation

Reserve sample sizes

Response with sample size

Ring systems size samples

Sample Gathering for Particle Size Analysis

Sample Size Issues

Sample Size and Shape

Sample broadening crystallite size

Sample cleanup size-exclusion

Sample preparation size limitation

Sample size

Sample size Bayesian approaches

Sample size adjustment

Sample size and geometry

Sample size approximate formula

Sample size assurance calculation

Sample size calculation

Sample size calculation issues

Sample size changing parameters

Sample size clinical importance

Sample size conventions used

Sample size cost issues

Sample size definition

Sample size dependence

Sample size design

Sample size determination

Sample size determining

Sample size drug products

Sample size effects, degrees

Sample size equivalence

Sample size example calculation

Sample size for

Sample size for clinical trials

Sample size inputs required

Sample size interim analysis

Sample size multiplicity

Sample size problems with

Sample size re-evaluation

Sample size reduction

Sample size regulatory issues

Sample size replication, experimental design

Sample size reporting calculation

Sample size review analysis

Sample size selection, environmental

Sample size selection, environmental sampling

Sample size significance with large trials

Sample size standard deviation

Sample size statistical process

Sample size survival data

Sample size with multiple requirements

Sample size, effects

Sample size, effects independent samples

Sample size, influence

Sample sizes and return rates

Sample-size estimation

Sampling error sample size

Sampling sample size

Sampling sample size

Sampling size-selective

Size and Shape of the Sample

Size exclusion chromatography sample preparation

Size of sample

Size-segregated sampling

Size-segregated sampling aerosols

Small sample sizes

Solid samples reducing particle size

Spectroscopic Methods Applicable to Different Sample Sizes

Standard deviation with sample size formula

Statistical analysis sample size

Statistics sample size

Study design sample size

Study design sample size estimation

Study protocols sample size estimation

Support Vector Machine Data Processing Method for Problems of Small Sample Size

Surveys sample size

The Control of Sample Size for Normal Preparative Column Operation

Thermogravimetry sample size

Treatment effects/differences sample size

Unequal sample size

Variable apparent sample size

Variables Involved in Sample-Size Estimation

Variance sample size

Water samples sample size

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