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The techniques used to describe the important characteristics of a set of data. This includes organizing the data values into a frequency distribution, computing measures of location, and computing measures of dispersion and skewness. |
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This facet of statistics deals with estimating a population parameter based on a sample statistic. For example, if 2 out of the 10 hand calculators sampled are defective, we might infer that 20 percent of the production is defective. |
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If one observation is greater than another by a certain mount, and the zero point is arbitrary, the measurement is on an interval scale. For example, the difference between temperatures of 70 degrees and 80 degrees is 10 degrees. Likewise, a temperature of 90 degrees is 10 degrees more than a temperature of 80 degrees, and so on. |
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The "lowest" level of measurement. If data are classified into categories and the order of those categories is not important, it is the nominal level of measurement. Examples are gender (male, female) and political affiliation (Republican, Democrat, Independent, all others). If it makes on difference whether male or female is listed first, the data are nominal level. |
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Data that can be ranked are referred to as ordinal measures. For example, consumer response to the sound of a new speaker might be excellent, very good, fair or poor. |
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The collection, or set, of all individuals, objects, or measurements whose properties are being studied. |
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If the distance between numbers is a constant size, there is a true zero point, and the ratio of two values is meaningful, then the data are ratio scale. For example, the distance between $200 and $300 is $100, and in the case of money there is a true zero point. If you have zero dollars, there is an absence of money (you have none). Also the ratio between $200 and $300 is meaningful. |
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A portion, or subset, of the population being studied. |
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The science of collecting, organizing, analyzing, and interpreting numerical data for the purpose of making more effective decisions. |
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