Quality Quiz from Professor Cleary
Lewis N. Clark considers himself an explorer when it comes to statistical process control. His job as deputy quality manager for a major health care equipment manufacturer puts him in touch with a great deal of data, and he likes to pursue his own version of data mining, which to him means making a variety of different visual expressions of the same data.
In the data that has come to his attention is a breakdown of end users of the firm’s adhesive patches used for a variety of medical tests, including electrocardiogram screenings (EKGs). Lewis decides to sort this data by age, since he believes that the information produced by a frequency distribution will be useful in the company’s marketing efforts.
Lewis makes the following frequency distribution chart:
Percentages of users of adhesive patches, by age:
|85 and older
|75 to less than 85
|70 to less than 75
|65 to less than 70
|60 to less than 65
|55 to less than 60
|50 to less than 55
|45 to less than 50
|40 to less than 45
|35 to less than 40
|30 to less than 35
|25 to less than 30
|20 to less than 25
|15 to less than 20
|10 to less than 15
|5 to less than 10
|less than 5
The data suggests some interesting conclusions that Lewis has already begun to make, based on his analysis of frequency distribution. For example, the largest group of users lies in the 75-to-less-than-85 age group, so more attention should be given to that customer base, he surmises. What has he done in creating this distribution that will render some of his conclusions inaccurate?
a) overlooked rules for creating class intervals
b) created groups that are too nearly equal in size
c) rounded off data
d) aggregated data so it has no meaning
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2009 PQ Systems.
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