| Beyond Deviation    Dr. Noah Tahl learned about using control charts, 
                      standard deviation and histograms in a two-hour workshop 
                      he attended during a national health care conference. He’s 
                      seen for himself how these concepts help hospitals and other 
                      health care institutions organize and analyze their data, 
                      and he’s determined to use statistical process control 
                      to improve quality measures at St. Maybe Hospital. In this 
                      effort, he trains his staff to ensure that all department 
                      personnel understand the basics of statistics and can use 
                      them accordingly.
  He’s determined to keep at least a page ahead of 
                      the group he’s training. So while everyone else is 
                      on the same page, Tahl stays up late to assure himself that 
                      he knows more than his trainees. It’s a never-ending 
                      struggle to stay ahead.  After he’s demonstrated the formula for calculating 
                      standard deviation and shown his trainees how control charts 
                      and histograms will help them understand processes, he introduces 
                      a software program that creates charts from data in established 
                      spreadsheets such as Excel. “This will make your life 
                      easier,” he promises, but as it turns out, it could 
                      very well make his own harder.  Alec Smart, one of his department managers, comes across 
                      the concept of the coefficient of variation as he explores 
                      the printout of his charted data. “What does it mean 
                      when the coefficient of variation is 11.92?” he asks.  Although the chapter on coefficient of variation isn’t 
                      one that Tahl has studied yet, he feels compelled to respond, 
                      especially because the entire class is looking expectantly 
                      at him. “That’s really the sum of the log of 
                      the standard deviation,” he mumbles. Although no one 
                      understands what he means, the trainees nod their heads 
                      and take notes.  Did Tahl provide the correct definition? And if so, is 
                      it important in health care? From the following, select 
                      the best description of the coefficient of variation: A. The capability of a process B. Whether a process is in or out of control C. The peak level of a distribution D. The ratio of the standard deviation to the mean  
  D is correct.  Coefficient of variation is a measure of how much variation 
                      exists in relation to the mean. Dr. Tahl’s guess was 
                      a meaningless collection of jargon. By obfuscating this 
                      simple technique, he was in fact robbing his trainees of 
                      the opportunity to gain another tool for data analysis.  Standard deviation alone isn’t particularly useful 
                      without a context. For example, knowing a standard deviation 
                      is 1.76 has no meaning, but understanding that a standard 
                      deviation of 2 had been anticipated provides a context that 
                      recognizes the variability is less than expected. Knowing 
                      the standard deviation has historically been 0.5 or less 
                      for a particular dimension, on the other hand, would suggest 
                      that 1.76 is considered high.  In examining the ratio of a standard deviation to a mean, 
                      the coefficient of variation provides a reference. 
  If the number is large, the data have much variability 
                      with respect to the mean. 
  A smaller number reflects a small amount of variation 
                      relative to the mean: 
  Michael J. Cleary, Ph.D., founder and president of 
                      PQ Systems Inc., is a noted authority in the field of quality 
                      management and a professor emeritus of management science 
                      at Wright State University in Dayton, Ohio. A 29-year professorship 
                      in management science has enabled Cleary to conduct extensive 
                      research and garner valuable experience in expanding quality 
                      management methods. He’s published articles on quality 
                      management and statistical process control in a variety 
                      of academic and professional journals.
 
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