Statistical Methods in Quality Management: Descriptive Statistics

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Quality management frequently uses statistical methods to identify the existence of a quality problem and to analyze the root cause of the problem. Statistical methods require the collection of numerical data related to a process under investigation. The data can then be used to identify trends that can affect quality such as the rate of variance in the outcomes of a production process. The descriptive or inferential analysis of the statistical methods can also provide information about the most likely causes of the problem. Statistical methods also have predictive value because they can identify potential problems before they have a significant impact on quality (Ryan, 3). Some of the statistical tools include descriptive statistics data such as frequency distributions, histograms, and inferential statistics analysis approaches such as regression analysis and analysis of variance (ANOVA). Each tool has advantages and disadvantages to their use. As a result, the use of the statistical tool often depends on the specific quality problem under investigation. Descriptive Statistics Tools and Histograms Descriptive statists provide a description of the the central properties of data obtained from observations. In quality management, the central properties can provide basic information concerning the amount of variance from a desired norm, which is a major advantage of using descriptive statistics. For example, descriptive statistics can provide information about the frequency of variance in desired tolerance that is greater than 10%, with less than 10% as the desired norm. In quality management, the descriptive statistical data that is of greatest interest is the central tendency, the dispersion, and the frequency (Madan, 268). In ad... ... middle of paper ... ...pes of information. At the same time, the disadvantages of the various statistical processes suggest that quality managers should use several approaches to analyzing data to ensure that their interpretation of the data is correct. Works Cited Christensen, Eldon, Christine Coombes-Betz, and Marilyn Stein. The Certified Quality Process Analyst Handbook. Milwaukee WI: Quality Press, 2007. Lighter, Donald and Douglas Fair. Principles and Methods of Quality Management in Healthcare. Gaithersburg MD: Aspen Publishing, 2000. Madan, Pankaj. Total Quality Management. Delhi: Krishna House, 2006. Ryan, Thomas. Statistical Methods for Quality Improvement. Hoboken NJ: John Wiley and Sons, 2011. Tari, Juan and Vincente Sabater. "Quality Tools And Techniques: Are They Necessary for Quality Management? International Journal of Production Economics, 92.3 (December 2004): 267-270.
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