Marketing Essay

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14 Descriptive Statistics Graphing and Summarizing Data The primary purpose of collecting data is to give meaning to a statistical story, to uncover some new fact about our world, and — last but certainly not least — to make a point, no matter how outlandish. But what do we do when we have too much data? One important purpose of statistics is to describe large amounts of data in a way that is understandable, useful, and, if need be, convincing. This is called descriptive statistics and is the subject of this chapter. Imagine that our data consist of the test scores of a group of students in a standardized exam. If we are dealing with a small group of students — say a class — then it is reasonable to look at the collection of test scores of the group and get the “big picture” (how the group performed compared with other groups, how many are at grade level, etc.). On the other hand, if we are dealing with a large group (hundreds, thousands, or even millions), trying to get to the big picture by looking at the individual scores of the students is hopeless. The amount of data to deal with becomes overwhelming — a huge babble of numbers. here are two strategies when trying to make some sense out of a large set of numbers. One is to present the data in the form of pictures or graphs; the other is to use numerical summaries that serve as “snapshots” of the data set. Graphical descriptions of data (bar graphs, pictograms, and pie charts) are introduced in Section 14.1. Section 14.2 is a brief detour into the types of variables that need to be considered when graphing data—categorical, numerical, discrete, and continuous. In Sections 14.3 and 14.4 we discuss the numerical summaries of a data set. Means, medians, quartiles, and percentiles tell us something about the numerical value of the data (they are called measures of location) and are discussed in Section 14.3.

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