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the selection of part of a total population of consumers or products whose behaviour or performance can be analysed, in order to make inferences about the behaviour or performance of the total population, without the difficulty and expense of undertaking a complete census of the whole population.

Samples may be chosen randomly, with every consumer or product in the population having an equal chance of being included. Random samples are most commonly used by firms in QUALITY CONTROL where they are used as a basis for selecting products, components or materials for quality testing.

Alternatively, samples may be chosen by dividing up the total population into a number of distinct sub-groups or strata, then selecting a proportionate number of consumers or products from each sub-group since this is quicker and cheaper than random sampling. In MARKETING RESEARCH and opinion polling, quota sampling is usually employed where interviewers select the particular consumers to be interviewed, choosing the numbers of these consumers in proportion to their occurrence in the total population.

Samples may be:

  1. cross-sectional, where sample observations are collected at a particular point in time, for example data on company sales and the incomes of consumers in the current year, embracing a wide range of different income groups, as a basis for investigating the relationship between sales and income;
  2. longitudinal, where sample observations are collected over a number of time periods, for example data on changes in company sales over a number of years and changes in consumer incomes over the same time periods, as a basis for investigating the relationship between sales and income. See STATISTICAL INFERENCES, QUESTIONNAIRE.
References in periodicals archive ?
In practice, DPCM system and predictor as its part, are imperfect and predictor coefficients have stochastic parameters with normal distribution [17], [19], [20], [24].
Beside first-order predictors, the most common used predictors in DPCM systems are of the second-order.
when DPCM predictor coefficients are not perfectly adjusted (their values are normally distributed around the projected value).
This sequence of quantized prediction residuals is passed through the standard DPCM decoder (as shown in Figures 7 and 8) to obtain the encoded sequence *, i = 1, 2,.
For comparison, the performance of a scalar DPCM system (reported in [17]) that uses the LS lattice predictor and the Jayant adaptive quantizer with one-word memory [10] is also listed.
r] there is a small decrease in SEGSNR, but its effect is minor in comparison to the gains achieved by PTCQ over DPCM.
If the conditions of sufficient mean deviation and normalized autocorrelation are met, DPCM starts by coding the differences between successive samples during the first period, and continues by coding the differences between samples one period apart afterwards.
Compression ratios were improved significantly in over 50 experiments with sine excitation in the 5 Hz-750 Hz range, sampled using 160 Hz and 2560 Hz sampling rates (compared to sequential samples DPCM preprocessing).
Average compression ratio changes, compared to sequential samples DPCM, vary from 1 % deprecation for cars induced oscillations of the tram bridge to 24 % gain on the vibrating platform.
DPCM is a technique of converting an analog into a digital signal in which an analog signal is sampled and then the difference between the actual sample value and its predicted value is quantized.
The block diagram of the DPCM encoder is shown in Fig.