Our goal is to take data that may contain thousands of observations and reduce it to a few calculated values. For example, we might calculate the average salaries of employees at several companies in order to get a general impression about which companies pay the most, or we might calculate the range of salaries at those companies to convey the same idea.
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Note that we should be very careful in drawing any general conclusions or making any predictions on the basis of our descriptive statistics. Those tasks belong to a different branch of statistics called inferential statistics, a topic we’ll discuss in later chapters. The goal of descriptive statistics is to describe the contents of a specifi c data set, and we don’t have the tools yet to evaluate any conclusions that might arise from examining those statistics.
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When descriptive statistics involve only a single variable, as they will in this chapter, we are employing a branch of statistics called univariate statistics. Now we’ve used the term variable several times in this book. Variables can be classifi ed as quantitative and qualitative. Quantitative variables involve values that come in meaningful (not arbitrary) numbers.
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Examples of quantitative variables include age, weight, and annual income—anything that can be measured in terms of a number. The number itself can be either discrete or continuous. Discrete variables are quantitative variables that assume values from a defi ned list of numbers. The numbers on a die come in discrete values (1, 2, 3, 4, 5, or 6). The number of children in a household is discrete, consisting of positive integers and zero.
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