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Statistics can be broken down into several main areas. There are three key categories: measure, process, and inference.[9] The measure of the variable of interest expresses the value of the respective variable; thus, measure concerns the value of a particular variable. The purpose of the measure is supposed to be to enumerate the numbers, or, more precisely, to make the enumeration. A measure can be either frequency (the number of occurrences), percentage (the number of occurrences as a portion of the total), or frequency density (the number of occurrences as a portion of the total area, or the number of occurrences per unit area; this last definition is sometimes known as population density).

A sample contains a finite number of observations (which may be from the same population). In the case of a sample population these are the given data on which you are going to form inferences. Sampling bias occurs if the sample under consideration is not a random selection of the population. Depending on the use, any sample can be described by a distribution of scores. The sample mean is the arithmetic average of the sample.

The mean of a sample is the most frequently emitted, or central, score. For a sample of scores exhibiting a satisfactory degree of homogeneity, the mean can be used as a good estimate of the score's range (location). For example, it can be estimated either as the average of all scores or as the average of upper and lower scores.

The relative weight or relative importance of a variable may be estimated in two ways: 1) the absolute weight is simply calculated as the percentage of the total weight attributable to a variable, and 2) the diversity (or lack thereof) of variables may be estimated by the sum of the weights of each variable. d2c66b5586