Summarizing Data

  • Gary L. Tietjen


This chapter is intended to introduce the basic ideas of statistics to the layman. Statistics is the accepted method of summarizing or describing data and then drawing inferences from the summary measures. Suppose, for example, that a company has made a process change in manufacturing its light bulbs and hopes that the new bulb (Type B) will have a longer lifetime than the old (Type A). Having experimented previously, the company knows that even though the bulbs are treated identically they will vary considerably (60 to 90 hours) in the length of time they will last. That variability, which is a property of almost all manufactured products, is called inherent variability. Since we cannot predict how long a bulb will burn, we describe its lifetime as a random variable. The company does know that the largest fraction of the bulbs burn about 75 hours, that those with lives of 70 and 80 hours are about equally frequent (but less common than lifetimes of 75 hours), and that those with lives of 65 and 85 hours are even less frequent.


Light Bulb Average Lifetime Exploratory Data Analysis Past Production Class Interval 
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  1. For the EDA techniques in this chapter see Tukey, J. W. 1977. Exploratory Data Analysis. Reading, Mass: Addison-Wesley. The classical techniques will be explained later in detailsGoogle Scholar

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© Chapman and Hall 1986

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  • Gary L. Tietjen

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