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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.
Collins Dictionary of Business, 3rd ed. © 2002, 2005 C Pass, B Lowes, A Pendleton, L Chadwick, D O’Reilly and M Afferson
References in periodicals archive ?
Digital Stochastic Measurement technique generally trades oversampling and large number of arithmetic operations for final accuracy.
Huang, "Frequency-domain oversampling for zero-padded OFDM in underwater acoustic communications," IEEE Journal of Oceanic Engineering, vol.
Hosseinzadeh and Eftekharia [25] learned Rotation Forest on the data obtained by preprocessing training set using the synthetic oversampling technique (SMOTE) [8] and fuzzy cluster [40].
In the implementation of SMOTE, there are two key parameters for controlling the amount of oversampling of the minority class and undersampling of the majority classes, that is, [alpha] and [gamma].
Eq.(14) says that regarding to the oversampling case, biorthogal analysis window function can be easily obtained once the synthesis function is set.
However, several aspects are not incorporated so far: IP oversampling replicates observations and by this biases the covariance structure within the strata.
According to Figure 4 which depicts values obtained for recall metric, classes C1, C2 and C25 have achieved the best results with Oversampling + Random Forest.
One is data-based strategy which aims to make the original dataset balanced using mainly undersampling or oversampling techniques.
Solutions to address this issue include oversampling and undersampling.
Thanks to the 20MP PureView camera with optical image stabilisation, oversampling technology and zooming capabilities, the Nokia Lumia 1520 brings personal stories to life with the new Nokia Storyteller and Nokia Camera.